Latest / From First Principles / Golden Goose Awards 2026: The Science Behind the Winners (Part 1) (EP 59)

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Golden Goose Awards 2026: The Science Behind the Winners (Part 1) (EP 59) · From First Principles

• 0:00
  A noise complaint, holiday lights, and a coffee stain.
• 0:05
  What could they possibly have in common?
• 0:08
  They are the unlikely beginnings of discoveries with unexpected benefits.
• 0:13
  And this year's Golden Goose Awards honors the federally funded research behind those stories.
• 0:19
  Junsu and her colleagues spent decades turning an unexpected lab result into a way to destroy liver tumors with sound.
• 0:28
  NASA's Black Marble team spotted holiday light patterns from space.
• 0:33
  The same tools that reveal cities brightening from celebrations can show them going dark after disasters and track power returning.
• 0:43
  And Sidney Nagel found deep physics inside everyday messes, shifting sand, splashing drops, and the ring left by a drying drop of coffee.
• 0:54
  Turning those surprises into something useful took years of experiments, sustained support, and people willing to keep asking questions.
• 1:03
  Today, we're exploring the science behind these discoveries and the questions that made them possible.
• 1:10
  This is From First Principles, and these are the Golden Goose Awards.
• 1:19
  Hello, Internet.
• 1:20
  This is your captain speaking, Lester Nare, joined as always by my co-host and our resident PhD, Krishna Chaudhary.
• 1:29
  This week is part one of our special coverage of the 2026 Golden Goose Awards organized by the American Association for the Advancement of Science, or AAAS.
• 1:44
  These awards are meant to shine a light on federally funded research that starts out as basic research and results in incredible practical applications, hence the name Golden Goose.
• 1:56
  By the time you are watching this, we will have attended the ceremony at the Library of Congress in Washington, D.C.,
• 2:04
  and have done interviews with all three of the award-winning research stories that we are going to cover today.
• 2:11
  Those interviews, along with our conversation with AAAS CEO Sudeep Parikh, will be coming out in part now.
• 2:19
  Today, we're going to be getting to know the researchers and unpacking the science behind their discoveries.
• 2:26
  As always, we are going to talk about the science from the ground up.
• 2:30
  And if you want to get the best coverage of the latest breaking science around the world, be sure to subscribe.
• 2:39
  Now, for our first story.
• 2:42
  Let's start with a noise complaint that helped set a remarkable discovery in motion.
• 2:51
  Our first story is From Noise Complaint to Cancer Treatment, recognizing Dr. Junshu, the Li Ka-shing Endowed Professor of Biomedical Engineering at the University of Michigan, UMICH.
• 3:09
  In the early 2000s, Shu was a graduate student in the late Professor Charles Kane's lab.
• 3:16
  Unfortunately, he passed away in 2020.
• 3:19
  They were investigating ways to remove heart tissue using ultrasound.
• 3:23
  And the idea was to help children who had congenital heart disease without open surgery, which would obviously be...
• 3:31
  Incredible.
• 3:33
  But the experimental setup was so loud that her lab mates started to make some complaints.
• 3:41
  So she tried changing the pulse sequence to quiet it down.
• 3:45
  And when she tested those pulses on pig hearts, she saw something remarkable, which was that the tissue was breaking apart.
• 3:55
  That observation helped launch more than two decades of work by Xu and her colleagues to develop histotripsy, one of my favorite names for an area of study, with funding support from the National Institutes of Health and the Department of Defense.
• 4:12
  Now, this is a great visual labeled bubble cloud.
• 4:17
  of what we're talking about here.
• 4:19
  So histotripsy uses short, intense pulses of focused ultrasound to create a cloud of tiny bubbles in targeted tissue.
• 4:26
  As those bubbles expand and collapse,
• 4:28
  They physically break the tissue apart, all without an incision.
• 4:33
  And that's what we're looking at here, which is just an incredible visual.
• 4:37
  Absolutely incredible.
• 4:38
  To understand.
• 4:39
  And the complexity of this just blows my mind.
• 4:42
  Yeah.
• 4:43
  So Shu and her colleagues co-founded Histosonics in 2009 to help bring this technology to patients.
• 4:49
  Again, starting from a place of just trying to make the lab mates happy because there's a little bit of loudness happening.
• 4:56
  Its Edison system received FDA authorization in 2023 for non-invasive destruction of liver tumors specifically.
• 5:05
  But the question is, how do you control something as violent as the collapsing of bubbles precisely enough to use it inside the human body?
• 5:16
  Yeah, I mean, it's an incredible question that first you have to, like, control the collapsing bubbles and then you have to move it around.
• 5:23
  If you go back to that video that you just showed, right?
• 5:27
  There's a bubble that you've somehow engineered using sound inside of the human body, which has tissue.
• 5:33
  So it's not like water where the speed of sound is the same everywhere, right?
• 5:38
  You've got to control where the bubble is.
• 5:39
  It can't be too big.
• 5:41
  Otherwise, it messes up all the other tissue that's nearby.
• 5:44
  You've got to control where it is.
• 5:46
  And you have to control the duration.
• 5:48
  Otherwise, it's going to kill...
• 5:49
  or cause heat and things like that.
• 5:53
  Fundamentally, it's kind of a physics question, right?
• 5:57
  How do you actually do that?
• 5:58
  So for this segment, I wanted to go through five papers that Chu co-authored along her journey from the 2004 all the way till now.
• 6:09
  She is obviously a professor now at the University of Michigan, but she's actually also an alumni.
• 6:15
  She did her master's and her PhD there.
• 6:17
  Yeah.
• 6:18
  And the story starts when she was a graduate student at the University of Michigan.
• 6:22
  Obviously, the University of Michigan hired her because they're like, yeah, she's going to win the Golden Goose Awards one day.
• 6:29
  So it's an early experiment where you have to control cavitation and then try to figure out how it's going to actually work.
• 6:39
  Right.
• 6:39
  So the first paper we're going to look at is in 2004.
• 6:42
  This is the original paper that kind of puts her on the map.
• 6:47
  The original medical problem was hypoplastic left heart syndrome.
• 6:52
  You alluded to this with the idea that some newborns, the left side of the heart is severely underdeveloped.
• 7:00
  And if the partition, you can see over there on the left is a normal heart, and on the right-hand side is a heart with hypoplastic left heart syndrome.
• 7:08
  The left ventricle and the left atrium are much smaller than the right-hand side, right?
• 7:14
  And...
• 7:16
  The way the heart works is you need both of these ventricles to pump blood in and out of the lungs, oxygenate it, and then put it back, right?
• 7:25
  There's one pump for the entire body, and that's the heart.
• 7:29
  And if the pump has like two motors you can think about and one of them is missing, it's not very good.
• 7:35
  On top of that, the connection valve between the atrium and the ventricle, oftentimes there's no hole.
• 7:45
  Like they're not even connected.
• 7:47
  So it's very, very difficult to even have a normally functioning heart, right?
• 7:52
  And that's why hypoplastic left heart syndrome, it's a defect of where the structures on the left-hand side are hypoplastic, meaning very small or not even formed at all.
• 8:02
  Okay, so what we'd like to do is create a little hole in that valve.
• 8:08
  so that it becomes a valve and it can open and close.
• 8:11
  Okay, so Zhu's group asked whether focused ultrasound might make an opening without putting a cutting instrument through the tissue.
• 8:20
  Which makes sense.
• 8:21
  It's basically, can we change the structure such that the functionality we're looking for is a possibility without too much second and third order consequences or damage to the other surrounding?
• 8:34
  Yeah.
• 8:35
  areas.
• 8:35
  Yeah, that's right.
• 8:36
  And in 2004, they have this paper that comes out, Controlled Ultrasound Tissue Erosion.
• 8:41
  It's in the IEEE
• 8:43
  conference papers and the paper does not establish a clinical treatment.
• 8:48
  Okay.
• 8:48
  It's just doing experiments on a model organism.
• 8:51
  In this case, canines before the bubbles, let's just think about sound waves.
• 8:55
  Okay.
• 8:55
  So ultrasound is a sound wave.
• 8:57
  It's a sound wave as an oscillation in pressure.
• 9:01
  And at, at a point in tissue, you can focus all the pressure and the pressure alternates between like some ambient value and then above and below that ambient value.
• 9:11
  Um, um,
• 9:13
  You might have heard about ultrasound therapy for cancer, for example.
• 9:17
  So in this case, what ends up happening is you focus the ultrasound using a transducer into a tumor.
• 9:26
  And those pressure amplitudes are there away from the region.
• 9:30
  They're not there.
• 9:31
  This is called acoustic focusing because you're taking sound and you're focusing kind of like a lens.
• 9:36
  And in this conventional focused ultrasound treatment, the usual picture is heat.
• 9:42
  in the middle.
• 9:43
  You just want to heat up that tumor and like burn it off, literally, right?
• 9:47
  The tissue absorbs that acoustic energy, the temperature rises, and then the sufficiently hot cells die off.
• 9:54
  So perhaps you can kill the tumor that way, right?
• 9:58
  Zhu's group was after another outcome.
• 10:00
  They wanted the tissue to disappear from a very sharply defined region, like an actual opening.
• 10:07
  You don't want to merely patch the tissue that's like destined to die later.
• 10:10
  You want to literally open it up.
• 10:12
  You want to destroy the mechanically rather than cooking it in some sense.
• 10:17
  Right.
• 10:18
  And you want it to be precise in 3D space because you're trying to create this opening.
• 10:23
  Exactly.
• 10:24
  It's not just some generalized region.
• 10:25
  Exactly.
• 10:26
  And so their experience, they did a bunch of experiments where they varied pulse duration, how often the pulses are repeated.
• 10:35
  Very short, intense pulses, they found, delivered at suitable repetition rates, eroded the tissue very effectively.
• 10:42
  And you could see this effect happening.
• 10:44
  You could see in the model organisms...
• 10:48
  you can see they're creating holes in the atrial septum.
• 10:51
  And then on the right-hand side, they use Doppler ultrasound to observe flow through it.
• 10:57
  For anyone who has been lucky enough to have a child
• 11:02
  you recognize the Doppler ultrasound.
• 11:05
  That's like when they, you look at like the, you literally use the Doppler effect.
• 11:10
  This is something that I saw when I was in the hospital, like looking at the, you know, the fetus that ultimately became my child.
• 11:17
  But you can look at, you can visualize blood flow by using the Doppler effect to see like stuff that's moving out and stuff that's moving in.
• 11:26
  Like there's literally, you can observe red and blue shifts.
• 11:29
  And the color coding is red and blue for like stuff moving away from you and stuff moving closer to you.
• 11:35
  And I was looking at that going, that's pretty cool.
• 11:37
  That's pretty cool.
• 11:38
  That's pretty cool, right?
• 11:39
  Usually you learn about that stuff in astrophysics when it's like redshift, blueshift, stuff moving away.
• 11:44
  But here they use the same colors to show like movement of fluid.
• 11:51
  And so just briefly as we...
• 11:53
  particularly for some of our audio listeners, we're looking at two sets of images.
• 11:57
  The one on the left is showing basically a before and after of this approach of the actual physical tissue.
• 12:03
  Yeah.
• 12:03
  Yeah.
• 12:04
  I think these are both after actually both after where it's now you're creating these cavities, like literal holes in the tissue.
• 12:11
  And on the right hand side, you're using the Doppler effect to show that fluid is moving through that tissue, which is ultimately the functionality that you're trying to restore.
• 12:18
  Exactly.
• 12:19
  Exactly.
• 12:20
  And the way they do this is with cavitation.
• 12:23
  So cavitation is the idea of using sound waves to create a bubble and then...
• 12:30
  precisely maneuvering that bubble, the size of it, and so on and so forth.
• 12:34
  And it's an intuitive distinction between cooking versus mechanical disintegration, which we talked about earlier, right?
• 12:41
  Cooking is what we do with the traditional ultrasound therapies, when we want to just, like, heat stuff up.
• 12:47
  Here, we're using a bubble to, like, expand and contract rapidly, and that's going to mess with all of the tissue around it.
• 12:54
  And it's incredible that we can do this with sound waves, right?
• 12:57
  Yeah.
• 12:59
  The material is still there, but the proteins in the structure have changed.
• 13:02
  And like with this histotripsy, you can use that pressure to start this cavitation.
• 13:09
  The bubbles expand and contract over these short pulses.
• 13:11
  This is incredible.
• 13:12
  And they create mechanical strain.
• 13:15
  It's very, very cool.
• 13:17
  It's such an interesting, you know...
• 13:19
  even conceptually as an idea, to think to do so as a sort of clinical approach.
• 13:29
  Exactly.
• 13:30
  So you've been able to create a bubble, right?
• 13:33
  The question now becomes, how do you control it really, really well?
• 13:39
  Right.
• 13:39
  So we were able to...
• 13:43
  instantiate this cavitation process.
• 13:45
  Yeah.
• 13:45
  Now I need to localize this thing.
• 13:47
  Yeah, now we need controllability.
• 13:48
  Right?
• 13:48
  We need really precise controllability for this to be a clinical application.
• 13:52
  So that's where we get into the next paper.
• 13:54
  This is a paper, again, in IEEE, Explore Histotripsy Beyond the Intrinsic Cavitation Threshold.
• 14:01
  using very short ultrasound pulses.
• 14:05
  So they call it microtrypsy.
• 14:06
  Microtrypsy.
• 14:09
  Here's the idea.
• 14:10
  So, like, you're using sound, right?
• 14:16
  And sound has a particular wavelength.
• 14:21
  Even this ultrasound is going to have a particular wavelength.
• 14:24
  But my target region could be much smaller than this wavelength.
• 14:29
  It's a question about resolution at the end of the day.
• 14:33
  Let's say I've got a focused region and I want to put a bunch of pressure amplitude using the sound into this area.
• 14:42
  It's largest near the center and then it falls off.
• 14:44
  Right?
• 14:44
  Right.
• 14:45
  So there's going to be a large area where I could produce bubbles and it becomes a stochastic process.
• 14:50
  So how do I control where the bubbles happen and where they don't?
• 14:56
  I need a very nice turning on and turning off.
• 15:01
  mechanism that's also specific again to an xyz position in 3d space yeah yeah but but here's the idea the only thing i can focus really is the sound waves right and the sound waves have an inherent blurriness right so so how do i how do i actually control where the bubbles are happening well it turns out cavitation naturally is a non-linear switch
• 15:27
  Okay.
• 15:28
  And that's what this paper is about that.
• 15:30
  So on the X, here's, we've got a plot from that paper.
• 15:33
  And I thought this was very, very cool.
• 15:35
  So on the X axis is your pressure.
• 15:38
  Okay.
• 15:39
  This is the pressure that you're creating using your ultrasound.
• 15:41
  And on the Y axis is the cavitation probability.
• 15:45
  Notice it is not a linear function.
• 15:48
  It's not like at 10 megapascals, there's like 20% probability.
• 15:52
  At 20, there's 40.
• 15:54
  At 30, there's 60.
• 15:55
  And it just linearly increases.
• 15:57
  No, there is a regime where it switches on and off.
• 16:01
  Mm-hmm.
• 16:02
  Okay?
• 16:03
  So what you can do, you can't control the spatial extent of your sound waves.
• 16:09
  But you can control how loud they are.
• 16:13
  Okay?
• 16:13
  Yeah.
• 16:13
  And if you can control how loud they are, imagine a wave, right, that's massive.
• 16:17
  Even, like, take the extreme case of a wave that has a crest that is a meter big.
• 16:23
  But if I can control the amplitude of this wave very precisely, how loud this wave is,
• 16:30
  The spots, even though the wavelength of my sound wave is like a meter long, the spots where...
• 16:37
  the pressure goes above a certain threshold can be as small as I want it to be.
• 16:41
  So this is a degree of freedom where we have a level of precision.
• 16:45
  And you basically use that as part of your first point of control.
• 16:48
  Yeah, because you can't control the wavelength of the sound in some sense, right?
• 16:52
  Like you're limited by the technology of our time and by the physics of the thing.
• 16:56
  I mean, if you make the sound wave too short, then the bubble...
• 16:59
  is not going to happen anymore, right?
• 17:01
  And the focusing capability is not going to happen anymore.
• 17:03
  So there's level of precision where we can know that we can create this height of the wave, and that height of the wave is a factor in this cavitation process that is a good control to have.
• 17:15
  Exactly, and the height of the wave is the x-axis on this plot, right?
• 17:19
  And so suppose the threshold is exactly, I don't know, over here it seems like to be 25%.
• 17:26
  mega pascals, right?
• 17:28
  So if the entire amplitude of the wave is only, let's say, 26 or 27, there's going to be a very tiny region that goes above that 26, right?
• 17:37
  27.
• 17:37
  Yep.
• 17:39
  If, on the other hand, I want to target a very large area, I make the sound super loud, like 40, and then most of the sound wave is going to go above that threshold.
• 17:47
  You see?
• 17:48
  So I'm controlling the space based on the loudness of the ultrasound.
• 17:52
  So if you want to make the speaker go to 11, you're going to take care of everything.
• 17:57
  Yes.
• 17:57
  But if you only make the speaker go to 4, it's going to be...
• 18:01
  It's a smaller area.
• 18:02
  It's a smaller area.
• 18:02
  And they show this with the next figure that I have here there.
• 18:06
  They've got, they tested this idea with red blood cell phantoms where you can clear the clearing can make the damage visible.
• 18:14
  And this is again in canine tissues at lower levels.
• 18:18
  So that's on the left-hand side.
• 18:20
  At lower pressures, the size of the cavitation is very small.
• 18:24
  But all I'm doing now is increasing the pressure.
• 18:27
  And as I increase the pressure and I'm focusing on this region, I can make the size of my cavitation much larger.
• 18:34
  Where the bubbles are happening are now much larger.
• 18:37
  And that is actually how I control localization.
• 18:40
  Yeah.
• 18:41
  So this is interesting.
• 18:42
  So I think part of what we're saying is the knob we have that we can twist in this context is do we want small bubble or big bubble?
• 18:51
  And depending on the use case, you can have multiple small bubble, multiple big bubble.
• 18:57
  But the point is that there's a dial for the size of the cavitation.
• 19:01
  I'm using bubble as layman's term.
• 19:03
  No, but it is.
• 19:04
  It's a bubble.
• 19:04
  Yeah.
• 19:05
  And we have very – the ability to have sort of fine control over that size is an important factor in, again, having functional control over whatever outcome we want to implement this cavitation towards.
• 19:20
  Exactly.
• 19:21
  It's like – it reminds me of like the mountain Kilimanjaro.
• 19:24
  Mm-hmm.
• 19:25
  Kilimanjaro is a massive mountain, the size of states in the United States, several European countries, is a single mountain.
• 19:34
  But the amount of ice on that mountain is only at the summit.
• 19:39
  And so you're not breaking diffraction.
• 19:42
  There's a diffraction limit similar to the diffraction limits that we've talked about on this podcast with light.
• 19:47
  where it's like I can't localize the light beam to a certain small amount because of the wavelength of light where in this case similarly the wavelength of sound prevents me from localizing the sound in a certain spot but I can make it loud and I can control the loudness the height of Kilimanjaro for example
• 20:05
  And if only the tip is above that threshold, then I can make stuff small enough for my liking.
• 20:13
  I thought that was pretty cool.
• 20:14
  That's quite nice.
• 20:15
  That's quite nice.
• 20:16
  Yeah.
• 20:17
  So that was that second paper.
• 20:19
  Second paper.
• 20:21
  Now, our attention has been very local, right?
• 20:24
  Like, where are the bubbles?
• 20:25
  Which cells are being broken up?
• 20:29
  Now let's talk about what happens when we try to target tumors.
• 20:33
  Right.
• 20:34
  Yeah.
• 20:34
  Okay.
• 20:35
  Right.
• 20:36
  What happens?
• 20:36
  What happens to the immune response?
• 20:38
  Because this stuff doesn't happen in a vacuum in the body.
• 20:42
  You start messing with stuff.
• 20:43
  There's the police are going to come.
• 20:45
  There's a lot going on and be like, what's going on?
• 20:48
  As we talk about a lot, biology is not so simple.
• 20:51
  Yeah.
• 20:52
  So we're going to, we're going to jump start all the way to 2020.
• 20:54
  2020.
• 20:55
  This is a paper called Non-thermal Hyptotripsy Tumor Ablation Promotes Abscopal Immune Responses That Enhance Cancer Immunotherapy.
• 21:08
  So it turns out that using these cavitation bubbles to take care of tumors actually overpowers the immune response that is innate within cancer.
• 21:21
  I think what's interesting too here, just maybe taking a step back, is immunotherapies are one tool in the toolbox to combat cancer and tumors.
• 21:35
  What we're saying here is that by using this methodology of cavitation, we're actually increasing the efficacy of our hammer.
• 21:43
  Yep.
• 21:44
  That we're using as a separate tool.
• 21:46
  And so there's this increased correlation effect.
• 21:49
  Yeah.
• 21:51
  That it…
• 21:53
  It has a direct functional output, but it also has this other benefit to other clinical therapies that we're using to solve, in particular, this issue around cancer.
• 22:06
  Exactly.
• 22:07
  And I want to talk about why that is.
• 22:09
  That's so good.
• 22:09
  Because I was thinking, like, you know, we've got the ultrasound therapies as well.
• 22:12
  I was talking about, right?
• 22:13
  Yeah.
• 22:13
  Where you just heat up the tumor and then you burn it off.
• 22:16
  Yeah.
• 22:16
  Yeah.
• 22:17
  How come this is somehow better?
• 22:19
  Because we have two things that both affect tumor structures.
• 22:22
  And they both use sound, ultrasound.
• 22:24
  And they both use sound, but why is it that this...
• 22:26
  This particular one is better.
• 22:27
  Okay.
• 22:28
  Well, it's that cooking versus disintegrating analogy that I was talking about.
• 22:34
  With the traditional ultrasound therapies, you heat up a tumor, right?
• 22:38
  And when you heat that up, that's going to burn off the tumor cells, the cancerous cells.
• 22:47
  You're going to break cells apart, but the components of those cells are still around.
• 22:52
  Yeah.
• 22:52
  And they're going to be available for immune recognition.
• 22:57
  You see what I'm saying?
• 22:58
  We're not deleting evidence from the crime scene.
• 23:00
  Yeah, we're not melting it.
• 23:01
  We're just like pulverizing it.
• 23:03
  But the immune cells can still, the police can still come and look at the evidence and be like...
• 23:09
  I probably should have been paying attention.
• 23:10
  Yeah, yeah, yeah.
• 23:11
  Right?
• 23:13
  You know what I mean?
• 23:14
  And so some of the components of those tumor cells that are broken down are tumor antigens.
• 23:20
  They're molecular features that an immune response is going to learn to target.
• 23:25
  On the other hand, if you just burn it off, those are now denatured proteins.
• 23:29
  The immune cells don't really know what they're looking at.
• 23:32
  It's the melted steel versus like intact stuff.
• 23:36
  Would an analogy for this be we have undercover immune agents embedded everywhere.
• 23:42
  When we burn them off, the undercover immune agents, when the immune response comes to the crime scene, they don't see them.
• 23:49
  And so they're like, our undercovers are gone.
• 23:52
  So we need to do something.
• 23:53
  Yeah.
• 23:54
  With cavitation, we're saying the undercover immune agents, they may not be at the spot they were assigned to, but they're in the vicinity.
• 24:00
  And there's like pieces of them.
• 24:02
  And you can still be, okay, okay, it's still here.
• 24:06
  And so I'm not going to overreact in my response.
• 24:11
  Yeah, but I'm at least going to learn what's going on here.
• 24:13
  Because I can pick it up.
• 24:14
  And I am going to heighten my response.
• 24:16
  I see.
• 24:16
  I'm going to like...
• 24:18
  be more vigilant that's a good distinction you know what i mean yeah yeah yeah no that's a good yeah exactly rather than being eviscerated yeah from the from the scene there's presence and you can then glean what your next action should be yeah because there's some presence because a lot of the immune system are detectives right they're not just like like dumb beat cops there's a lot of people that have like there's a lot of detectives that have memory that have
• 24:43
  the ability to take antigens, recognize them for the rest, so that if the tumor shows up elsewhere or something, and that's something that we're going to get to.
• 24:51
  We got some immune columbos on the scene.
• 24:53
  Yeah, yeah, yeah, exactly.
• 24:55
  And so this histotripsy, it's associated actually with the infiltration by innate and adaptive immune cells.
• 25:03
  in the treated tumor, including tumor-reactive T-cell responses.
• 25:07
  So in the control, you have just normal ablation.
• 25:11
  And when you use histotripsy, the concentration of these T-cell receptors, the CD8 plus T-cells,
• 25:22
  they go up.
• 25:23
  This is a CD4 T cells.
• 25:26
  Like all of these guys that are like geared to learn about what the tumor is and make sure that that tumor does not show up elsewhere.
• 25:34
  It's going up.
• 25:38
  It's so interesting because I don't know that this outcome is obvious.
• 25:43
  Mm-mm.
• 25:44
  you know but it's not i mean now that i think about it sure right but like in 2000 when when you know like fiber however many 10 10 years ago when like this cavitation was being used not obvious at all i think and it's really it it's such a nice also because again we always talk about how
• 26:05
  there's a building block process to get to ultimately something that is viewed as practical or functional for humanity.
• 26:13
  And it's like, hey, look, we have this new functionality.
• 26:16
  Just because you have functionality doesn't mean it has practical application.
• 26:20
  Yeah, and this has more practical applications than what you even started out with.
• 26:23
  I mean, you started out with just like, why is the sound bad?
• 26:26
  Into, okay, let's like try to burn a hole in the left ventricle.
• 26:31
  Mm-hmm.
• 26:31
  to wait this can supercharge cancer cancer therapies right right right that are already existing and what what folks who are dealing with this have to do yeah to stand a fighting chance and you're basically saying oh yeah oh by the way yeah
• 26:49
  it's going to make the thing you're already doing, which is, it's tough on the, it's complicated.
• 26:53
  There's a lot, but it's, it's going to make it more effective that I'm, I'm just, I did not expect that outcome.
• 26:59
  Yeah.
• 26:59
  I didn't expect this one either.
• 27:00
  And just one more figure from this paper is, it shows that the effects on the tumors outside of the treated site.
• 27:07
  Okay.
• 27:08
  So you're treating with cavitation with this histotripsy in a certain spot.
• 27:13
  And now you look at pulmonary metastases.
• 27:18
  elsewhere okay so so lung cancer happening maybe in the other lung or like there's this cancer happening here did it travel to the lung right um how is it moving out because the immune response has been supercharged right the the ones that are treated with the histotripsy versus the control
• 27:41
  Night and day difference.
• 27:42
  The ones that are treated with histotripsy have a much higher suppression of these pulmonary metastases.
• 27:52
  The immune response sets up a tighter perimeter, which then prevents the spread more than if it had not happened at all.
• 27:59
  Yeah.
• 27:59
  I mean, this is really, really cool.
• 28:00
  I thought this was the most interesting conceptual turn.
• 28:03
  And then like a bubble cloud has become a biological instrument.
• 28:07
  Yeah.
• 28:07
  Right.
• 28:08
  In the first paper, it was just like, okay, we want to remove tissue.
• 28:10
  Yeah.
• 28:11
  Here, the same kind of mechanical intervention can change the information available to the immune cells throughout the organism in the entire body.
• 28:21
  So this is like a definition of like a precise surgical tool that is doing way more than what we paid for.
• 28:28
  Way more than just the initial precise surgical outcome that you were looking for.
• 28:32
  That's right.
• 28:33
  So now we get to our fourth paper in 2021, transcranial magnetic resonance guided histotripsy for brain surgery.
• 28:41
  We are not just done with cancer.
• 28:42
  We're going to go to brain surgery.
• 28:44
  This is a preclinical investigation by Zhu and her colleagues.
• 28:47
  They tested whether they could make controlled lesions behind pig brains
• 28:52
  inside pig brains while transmitting through an excised human skull.
• 28:57
  This is a human skull that has been donated for medical research.
• 29:02
  So this is a preclinical experiment and the geometry is worth explaining.
• 29:07
  So you've got the pig skull which is the model organism and there's a human skull around it.
• 29:12
  So we want to test whether this ultrasound can go through the human skull and actually do brain surgery on neural tissue inside.
• 29:19
  The skull is notoriously a terrible interface.
• 29:23
  Okay.
• 29:24
  I dealt with this as a PhD student, right?
• 29:26
  Like if you do, if you do just ECOG, ECOG data is terrible.
• 29:31
  Okay.
• 29:32
  Look, I'm sorry.
• 29:33
  It's not terrible.
• 29:34
  It's not terrible.
• 29:35
  But like I'm an e-fiz guy, which means like it's invasive, but I had to do it on rodents, obviously, because I wanted high fidelity, 40 kilohertz data.
• 29:44
  I want 40,000.
• 29:46
  data points per second so that I can localize single neurons and things like that.
• 29:50
  Right.
• 29:51
  You wanted 4k, you're getting 480p.
• 29:53
  Yeah.
• 29:54
  Like, no, it's like a 20 and really like ECOG data, like the stuff that you see of like the, you know, patients with electrodes on their skull.
• 30:04
  Even if you had, even, even if you put that ECOG wires on a data acquisition system that was taking data at 40,000,
• 30:14
  hertz, like 40,000 data points a second, it would be a total waste because all frequencies higher than like 40 hertz are
• 30:23
  Don't pass through the brain.
• 30:25
  So you would just see giant waves like this, right?
• 30:28
  Any blip that is like a millisecond long, which is about the size of an action potential, is not going to make it through the skull.
• 30:35
  So there's no point in like taking data at that fast of a rate.
• 30:39
  And I think part of the – so what I'm understanding, part of the point here is ultimately if we want to take this from the experimental environment to being functional for humans –
• 30:49
  Anything that's passing through a skull matters.
• 30:51
  And the human skull specifically is the barrier that we need to make sure that this actually gets through.
• 30:57
  I mean, there's a very good reason, right?
• 31:00
  The brain is a nice thing that we have evolved over the last 10 million years.
• 31:04
  And the skull has been evolved to take care of that brain.
• 31:08
  Okay.
• 31:08
  So, like, I'm not saying the skull is a bad thing.
• 31:11
  I'm just saying it's a little bit annoying for basic science research.
• 31:15
  and for basic medical procedures so the skull is a difficult electronic device as i just told you but it's also difficult acoustically for the same reason so like ultrasound is there's going to be impedance from the skull and so and so there's going to be problems right it attenuates sound and the thickness and the internal structure vary across the beam like there's skulls can be thick here and and thinner on other parts
• 31:38
  So different paths through the bone delay the wave by different amounts.
• 31:42
  You're going to get like weird diffraction signatures when the ultrasound goes through the brain.
• 31:47
  So all of that has to be dealt with.
• 31:51
  These are engineering problems as much as biological ones if you want to use ultrasound.
• 31:56
  And precision matters as we've discussed.
• 31:59
  Exactly.
• 32:00
  So what these guys did was they used 128 element 700 kilohertz array.
• 32:03
  Okay.
• 32:03
  Okay.
• 32:07
  Compatible with a three Tesla MRI scanner.
• 32:09
  So they've got an MRI scanner at the same time that they're administering ultrasound.
• 32:14
  Why?
• 32:15
  Because the ultrasound, they're trying to mess with the brain that's on the inside.
• 32:18
  The MRI scanner is their read.
• 32:21
  They want to see what it was like before, and then they mess with it with the ultrasound, what it's like after.
• 32:26
  Can I localize my pulses, right?
• 32:29
  Turns out they can.
• 32:30
  Okay.
• 32:31
  They had successful treatment in eight pig brains, and the MRI treatment regions show that.
• 32:36
  They actually also did histology, which is the real ground truth, I think.
• 32:40
  Histology meaning you take out the brain, you put it into slices, you stain it, and then you can really see what's happening in the tissue, right?
• 32:46
  And this is a slice of that brain.
• 32:49
  And there's clear demarcation, okay?
• 32:51
  The D is the disrupted debris from my target.
• 32:56
  Oh my goodness.
• 32:57
  The eye is the intact neurons.
• 32:59
  And you can see just how sharp the interface is.
• 33:02
  Yeah.
• 33:03
  Right?
• 33:03
  Between the part of the brain that you wanted to ablate and sort of disrupt and the part that you wanted to keep intact.
• 33:11
  The scale bar there is 100 microns.
• 33:13
  This is unbelievable.
• 33:14
  Incredible.
• 33:14
  I mean, the thickness of that black line there, I would say tops is 10 microns.
• 33:19
  I'd even say maybe one to five.
• 33:22
  That's the size of large neurons.
• 33:25
  Okay, maybe groups of small neurons.
• 33:27
  And so we're now getting conformational data that are the level of precision when going through a human skull of this ultrasound approach has micron level...
• 33:42
  Thick.
• 33:43
  Precision.
• 33:44
  Precision.
• 33:45
  That's right.
• 33:46
  And we built this, right?
• 33:47
  Because in the beginning, we were just doing bubbles.
• 33:49
  And then we built, okay, how do we make sure that the precision is there?
• 33:54
  Well, we can actually tune the amplitude of the pressure wave.
• 33:57
  And now we can actually do it within the skull.
• 33:59
  We can deal with all of the diffraction and all of the weird wave stuff that's happening on the skull.
• 34:04
  Yeah.
• 34:04
  And it seems like the skull is a region because obviously we've targeted different parts of the body over the course of this discussion.
• 34:10
  The heart, we're going to get to other places, but the brain now is another.
• 34:15
  And the TSA of the brain is pretty tough to get through.
• 34:21
  Yes, exactly.
• 34:25
  One other thing I want to just touch on here before we move on to our fifth paper.
• 34:30
  Why use histotripsy rather than just heating for this job?
• 34:35
  Well, it's in the name itself.
• 34:38
  You don't want to heat the brain.
• 34:40
  The brain is incredibly conductive.
• 34:42
  There's fluid everywhere.
• 34:43
  And so if you heat part of the brain, that heat is going to travel.
• 34:48
  Thanks to the Navier Stokes sequence.
• 34:49
  Which we talked about.
• 34:51
  Tune into our last episode for that one.
• 34:53
  There's going to be a lot more Navier Stokes when we talk about Sidney and his physics stuff.
• 34:59
  But for now...
• 35:01
  Because fluid can sort of transport energy very well, that's a really bad thing.
• 35:05
  On the other hand, histotripsy, the pulses can have a very low duty cycle.
• 35:10
  A duty cycle is, imagine you've got a sine wave, right?
• 35:13
  A sine wave is exactly half duty cycle.
• 35:15
  It's one up, one down.
• 35:17
  Just up and down.
• 35:19
  But you can imagine...
• 35:22
  that's a really small duty cycle.
• 35:24
  Because the amount of time that something is on is very short compared to the amount of time something's off.
• 35:29
  The frequency can still be the same.
• 35:32
  But the part of the wave that is above threshold and the part of the wave that is below threshold, that ratio is called the duty cycle.
• 35:39
  And for these cavitation experiments, the duty cycle can be very low, which means that you're only actively sending this high-pressure ultrasound not that long.
• 35:49
  Right?
• 35:49
  Yeah.
• 35:50
  And so you're not heating up the skull that much.
• 35:53
  It circumnavigates the skull heating problem.
• 35:57
  And you can really target that mechanical disruption that you're talking about without really heating up the brain tissue in the first place.
• 36:04
  The idea is a longer duty cycle would mean we're doing too much, causing too much chaos.
• 36:09
  Chaos creates heat, creates more destruction than intended.
• 36:12
  So the fact that we do have a short duty cycle is actually a benefit to the functionality that we're trying to accomplish.
• 36:19
  Yeah, exactly.
• 36:21
  And so...
• 36:23
  There's still something that is lacking here, which is fully like now we're trying to do it on straight up human beings.
• 36:32
  Yeah.
• 36:33
  Okay?
• 36:33
  Yeah.
• 36:34
  Like we've proven it in all these model organisms.
• 36:38
  We've proven it with sort of a half model organism and a donated human skull.
• 36:43
  But now can we actually do this very precisely?
• 36:46
  Can we compensate for the variable acoustic medium?
• 36:49
  The brain, you know, once you get past the skull, the brain is kind of,
• 36:53
  Same, same.
• 36:54
  But other parts of the body, there's organs everywhere.
• 36:57
  Yeah.
• 36:57
  Right?
• 36:58
  So there's going to be variable acoustic medium.
• 37:00
  Can you maintain a useful pressure focus and generate bubbles exactly where you intend?
• 37:04
  That's actually a really good point.
• 37:06
  All these environments have very different variables you're dealing with.
• 37:09
  Yeah.
• 37:09
  Yeah.
• 37:10
  And that's why the last paper that I'm going to focus on is the one in the liver.
• 37:14
  This is in 2024.
• 37:16
  It's a clinical paper.
• 37:17
  So this is, you know, now we're getting into like human clinical trials.
• 37:23
  The HOPE4Liver Single Arm Pivotal Trial for Histotripsy of Primary and Metastatic Liver Tumors.
• 37:33
  She was a co-author.
• 37:34
  This is a multi-institution study.
• 37:36
  And the engineering now has changed form.
• 37:39
  We started from just making holes to now we're doing...
• 37:43
  There's a clinical transducer.
• 37:44
  There's an ultrasound imaging probe for targeting.
• 37:47
  Yeah.
• 37:47
  There's a software to describe the treatment volume.
• 37:50
  There's a robotic arm that moves the focus through that volume.
• 37:54
  This is crazy.
• 37:55
  They've got like the thing that is doing the ultrasound is like moving around in a robotic arm to like target stuff.
• 38:02
  And each tiny focal treatment has to be assembled into a three-dimensional area that covers a tumor that is in the liver.
• 38:10
  So we're now getting to let's make this work for people.
• 38:14
  Exactly.
• 38:15
  Yeah.
• 38:15
  So there's like margins that you have to plan.
• 38:18
  There's nearby structures that you got to avoid.
• 38:20
  This is now an intact like human being.
• 38:23
  Yeah.
• 38:23
  Okay.
• 38:23
  This is a real deal.
• 38:25
  Like and the technical success, the definition of technical success is whether the resulting treatment zone completely covered the intended tumor volume.
• 38:37
  on the contrast-enhanced CT or the MRI within 36 hours.
• 38:42
  Like, I do the treatment, and within 36 hours, when I take the MRI, do I see the effect, right?
• 38:49
  Well, let's see.
• 38:50
  Let's see if we do.
• 38:51
  In fact, we do.
• 38:52
  So this is figure four from their paper.
• 38:55
  It shows a 1.3-centimeter metastatic colorectal cancer in segment three of the liver.
• 39:02
  That's the white arrow on the top left.
• 39:04
  There's a little tiny tumor there.
• 39:07
  Okay?
• 39:08
  These are MRI scans.
• 39:10
  So in B, this is 36 hours post-procedure.
• 39:14
  The treatment zone is in the solid arrow encompassing the side of the tumor, and there's a margin.
• 39:20
  Crucially, the smaller arrow there, that's a blood vessel that traverses the treatment zone.
• 39:26
  It hasn't been passed.
• 39:29
  Yeah.
• 39:29
  Yeah.
• 39:30
  Okay.
• 39:31
  The traffic is still flowing.
• 39:32
  Yeah.
• 39:33
  We just took apart the riffraff around it.
• 39:36
  But the traffic of the blood is still flowing through that.
• 39:40
  And then in part C, this is 30 days post-procedure.
• 39:43
  It shows involution of the treatment zone, meaning the giant hole that you made has gotten smaller.
• 39:49
  There is recovery happening within the tissue.
• 39:53
  Yeah.
• 39:53
  Yeah.
• 39:54
  Yeah, the region we've targeted is now repairing, and we still have our traffic flow.
• 40:00
  Yes, exactly.
• 40:03
  It's an incredible story.
• 40:05
  Yeah.
• 40:06
  I mean, it's incredible for, you know, obviously so many people in every, you know,
• 40:11
  Many listeners are less than two or three degrees of separation away from someone who deals with or has suffered from cancer actively or in the past.
• 40:23
  Yeah.
• 40:24
  And obviously this has been an area that has had.
• 40:28
  incredible amounts of human scientific research as well as financial backing to try to find ways to deal with.
• 40:35
  There's so many interesting aspects of this area of research that, again, the construction, the building of, oh, hey, this methodology has functional benefit.
• 40:52
  Oh, hey,
• 40:58
  Oh, hey, we can actually do it in human-like contexts.
• 41:04
  Oh, and now when we get to the point where we have to do this in a very precise way for clinical outcomes, when we look to see the results, we are seeing that what we thought was maybe possible at the beginning of this journey is proving to be true.
• 41:20
  Yeah.
• 41:21
  It's incredible.
• 41:22
  And I mean, I don't want to oversell this thing.
• 41:25
  I do want to, I do want to say that, you know, cancer is an extremely multifaceted disease, ailment.
• 41:34
  You know, 95%, they had 95% success.
• 41:37
  Three out of the 44 patients actually did have major complications related to the surgery.
• 41:42
  So something like 7% had major, major complications.
• 41:48
  And yeah,
• 41:50
  One of the limitations of this is that you need to be able to image this tumor in the first place very, very well.
• 41:57
  There's some tumors where it's like impossible to image because based on their placement and where they are and so on and so forth, right?
• 42:03
  So, but in its limited capacity, it could be doing wonders, right?
• 42:07
  It could be the next step.
• 42:09
  And the treatment for cancer in the future is never going to be a one- Right.
• 42:16
  one thing rules them all, right?
• 42:18
  Unless we finally, like, everyone gets genetic engineering somehow, right?
• 42:23
  Like, and we all live in Gattaca, and it's, like, mandatory.
• 42:27
  Other than that scenario, like...
• 42:29
  Dealing with cancer is going to be a multifaceted.
• 42:32
  There's going to be immunotherapy drugs.
• 42:33
  There's going to be like chemo is always going to be around, I think.
• 42:39
  But more and more tools are necessary, right?
• 42:42
  And more and more tricks that we have.
• 42:44
  The better tricks we have, the better it is.
• 42:47
  In 2023, histosonics...
• 42:50
  received US FDA de novo clearance for destruction of liver tumors based on this study.
• 42:55
  Because even that 7%, that was like lower than the threshold that is required for the FDA to approve something.
• 43:01
  And this marked a clinical application of this technology developed through CHU's collaboration with these engineers, physicians, and many other researchers.
• 43:09
  This 2024 trial helps explain the evidence behind that step, right?
• 43:14
  And this explains why now the FDA is approving this.
• 43:18
  In 2025, Histosonics announced a management-led transaction, valuing the company at approximately $2.25 billion.
• 43:27
  That's billion with a B.
• 43:29
  With a B. Yeah.
• 43:30
  And that's the company evaluation.
• 43:32
  I don't think it's a measure of any scientist personally and how much they earned.
• 43:36
  I don't think Zhu earned a giant significant portion of that.
• 43:42
  Which is problematic, but that's for another episode.
• 43:44
  Yeah, exactly.
• 43:45
  But it's one sign that, you know, this approach that can begin as a difficult laboratory question, it now sits as a real medical and commercial ecosystem.
• 43:55
  There are so many interesting aspects of this story.
• 43:57
  And again, part of what the Golden Goose Awards tries to do, as you've seen in our first story here through the progression, is when we look at and we've talked about federal funding for basic research on this podcast quite a bit.
• 44:12
  There is a difference between basic research and applied research.
• 44:15
  We're trying to increase our understanding because from that, we are so brilliant as human beings, so creative.
• 44:22
  We have an ingenuity that when we find these unlocks, as we've talked about, we will find the practical path.
• 44:31
  Yeah.
• 44:32
  This has led to for in terms of folks focus on the economic outcomes of this investment of taxpayer dollars towards some of this stuff.
• 44:42
  You know, there's a $2.25 billion acquisition that's happened in the last 12 months.
• 44:47
  There are huge real world human benefits from the clinical application of this, which is why it's been valued so high.
• 44:59
  And.
• 45:00
  It's a platform.
• 45:03
  not necessarily just a single functional outcome.
• 45:06
  The liver cancer treatment pathway happens to be the pathway that we've gotten to the place where there's commercial and practical applications.
• 45:16
  Are there potential practical applications in other areas of cancer and applications for immunotherapy yet to be seen?
• 45:26
  But as we kind of discussed, the breadth and scope of potential
• 45:31
  It's quite wide.
• 45:34
  And it is a perfect starting story for understanding what are the Golden Goose Awards and what are they trying to highlight.
• 45:45
  It's this idea that we should not lose our curiosity as we look at these things because applied research and development in the United States –
• 45:58
  dwarfs by orders of magnitude basic research.
• 46:00
  Yeah.
• 46:01
  And had we not had funding for a project like this, as well as the rest of our stories, there are real world benefits that we would have left on the table that were not necessarily obvious in this case because the roommates were complaining about
• 46:16
  noise as a former dj i know what that feels like too but i'm not saving people's lives in the way that uh this story really exemplifies it's just it's really incredible and the science is also super interesting on this one um it's super super interesting yeah i will we'll maybe follow up on this in a future show but yeah and i just had an idea for a comment yeah if if you guys are
• 46:46
  about the stuff that your lab mates complain about because i've got several yeah just just put down if if in case you're like a phd student or a postdoc in your lab and just the complaints that lab mates do you know because who knows who knows you may both save people's lives and be the recipient of a future golden goose award yeah um
• 47:12
  An incredible first story.
• 47:13
  Hopefully for our listeners who may not be familiar, you kind of get an understanding now of what these awards really are about.
• 47:22
  And before we get into our next story, we're going to take this opportunity to do some quick housekeeping.
• 47:28
  For many of you who may be first-time listeners, welcome to the best science show on the planet.
• 47:34
  For those of you who are returning, thank you for joining FFP Nation and continuing to support the show.
• 47:42
  Any platform you can think of, our videos are both on YouTube and Spotify currently.
• 47:47
  For audio listeners who may be listening on Apple, we are waiting for Apple to turn on video, which allegedly is happening before the end of the year.
• 47:55
  If you're interested in some of the research papers that we talk about on the show, you can check out our website, ffppod.com.
• 48:02
  Every episode page always includes the research papers we discuss in each episode.
• 48:07
  There's a lot of great other features on the site, including particularly relevant to this episode, the U.S. Funding Tracker for Research and Development.
• 48:16
  And so you can really dig into several different components about research.
• 48:22
  decades worth of funding as it relates to R&D across dozens of agencies.
• 48:28
  You can see how it's increased or decreased over time.
• 48:31
  You can see how the current funding cycle is shaping up for fiscal year 2027 with explainers, graphs, charts.
• 48:39
  It's actually a really great way for you to ground yourself in how much are we spending on how much we're spending.
• 48:43
  in these areas and where is it going to?
• 48:45
  Because interestingly, as we've talked about recently, many people think of the National Science Foundation as like all this money is going there.
• 48:52
  And from a research and development perspective, they're actually a very small slice as compared to some other agencies.
• 48:58
  I would have you guess which those other agencies may or may not be.
• 49:03
  But we really appreciate you all who are listening and joining us, especially for this super special episode in what is going to be an incredible couple of weeks.
• 49:13
  We are going to jump in to our second research story for the Golden Goose Awards.
• 49:20
  Our next story is tracking holiday lights from space, recognizing NASA's black marble team.
• 49:28
  In 2012, researchers studying satellite observations of Cairo noticed an unexpected jump in nighttime brightness.
• 49:36
  They traced it to Ramadan when meals, shopping, and social gatherings shifted into the night.
• 49:41
  And they also found holiday brightening around Christmas in U.S. cities.
• 49:46
  Patterns of human activity were showing up in satellite data.
• 49:51
  With NASA's funding support, which is one of the most popular federal agencies currently in the U.S. government, the team's broader work produced Black Marble, a system that turns NASA
• 50:02
  nighttime satellite observations into consistent measurements of lighting on the ground.
• 50:09
  And the same ability to measure those changes also helps to know, for example, when the lights go out.
• 50:16
  And
• 50:19
  is comparing Fort Myers before and after Hurricane Ian in 2022.
• 50:25
  Areas that had been brightly lit became much darker as the storm knocked out power.
• 50:30
  And this kind of makes intuitive sense conceptually.
• 50:33
  By following these changes over time, researchers can map outages and track recovery and,
• 50:39
  helping identify communities that may need support.
• 50:42
  Obviously, if the lights come back on, you don't have to send first responders into that area necessarily because we've now restored power.
• 50:50
  But how do you tell whether a place has actually lost power or whether clouds or moonlight or other atmospheric effects are changing what the satellite sees?
• 51:01
  This is the setup for our next Golden Goose Award winner.
• 51:06
  That's right.
• 51:07
  So in September 2017, if we take another specific example, Hurricane Maria crossed Puerto Rico, and the electrical grid failed across much of the island.
• 51:17
  And you can see a before and after.
• 51:18
  It's almost like the whole island goes dark.
• 51:21
  And from orbit, the change is immediate, right?
• 51:23
  Places that admitted light night after night suddenly go dark.
• 51:29
  Now, scientifically, there's an interesting question about,
• 51:32
  And it's not simply whether the island went dark, it's how the power returned, right?
• 51:38
  Street after street, month by month, and whether some communities were left waiting longer than others.
• 51:44
  Now, that question can't be answered simply by taking a before and after and after and after photo.
• 51:51
  Because there's a lot that happens between a satellite getting light from an area and...
• 51:58
  and that light being attributed to human activity.
• 52:03
  Okay?
• 52:04
  So Miguel Ramon, Eleanor Stokes, Xu Shen Wang, Virginia Kolb, and Ranjay Shrestha and their collaborators, they've been working on a way to measure artificial light from space with enough care to answer questions like that.
• 52:19
  How much of this is because of humans?
• 52:21
  And this is NASA's Black Marble.
• 52:24
  It begins with a...
• 52:26
  remarkably sensitive satellite camera.
• 52:29
  And the real work is actually taking the measurements from the satellite camera and turning it into how much of this is human activity, how much of this is a different moon phase, how much of this is a cloud, a different viewing angle, snow on the ground, all of these other effects, taking all of that out and focusing directly on human activity.
• 52:49
  So in the same tradition that we talked about the previous one, we're going to do five papers that are connected to this group.
• 52:59
  It's an incredible, incredible set of papers, and we'll keep the distinction in mind throughout, that the satellite measures light, but what we are interested in is human activity, right?
• 53:09
  There's a longer history here because military weather satellites have revealed city lights decades before black marble.
• 53:16
  There's a joint NASA and NOAA Suomi NPP satellite that had the VIIRS instrument.
• 53:25
  This was launched in 2011.
• 53:28
  And this has a day and night band.
• 53:30
  Its sensitivity made a new class of nighttime observations possible.
• 53:34
  And in 2012, we got this striking image of Earth at night.
• 53:40
  I remember, actually, this was one of the first big images of Earth at night.
• 53:44
  And you could see some very stark differences in.
• 53:50
  based on geography.
• 53:51
  One of the big ones that was very famous was that if you looked at North Korea versus South Korea, it was massive.
• 53:57
  Like South Korea has lit up so much city lights.
• 54:02
  And then there's a band which is the demilitarized zone, which is an extremely bright ribbon in between South Korea and North Korea.
• 54:11
  And then in North Korea, there's just really one kind of tiny pocket compared to South Korea.
• 54:15
  And that tiny pocket is Pyongyang, which is so much smaller than Seoul, which is just across the border.
• 54:21
  And you could just see the stark difference between.
• 54:25
  between the lighting that tells you so much about the economic activity and about the people and the regimes there, right?
• 54:32
  So that was an early, wonderfully specific observation.
• 54:36
  And Ramon and Stokes studied how this nighttime lighting changed during Christmas and New Year's in the United States and during Ramadan, as you were saying.
• 54:45
  And they showed that lights during Ramadan and Eid, the green in this plot...
• 54:52
  is an upscaling of nighttime lights.
• 54:56
  And you can see that in Amman, Georgia, and in Cairo, there's a giant upscaling of lights that happen during the nighttime, during Ramadan, and during Eid.
• 55:06
  Which shows so much about the people who live there and their ways of life and things like that.
• 55:11
  So this already tells you that there's variations between places that these satellite observations might capture.
• 55:17
  And these satellite observations can tell us about human activity, the locations of cities.
• 55:22
  And it's an open scientific question how far that idea can be taken.
• 55:27
  Yeah.
• 55:27
  Right?
• 55:29
  And so that's where we're going to start with paper number one in 2018.
• 55:33
  This is Ramon.
• 55:34
  He's the first author.
• 55:37
  Roman, sorry.
• 55:38
  Roman is the first author.
• 55:39
  He was called NASA's Black Marble Nighttime Lights product suite.
• 55:47
  It's just a product suite that showcases all of the capabilities that this team has created.
• 55:55
  Ultimately, especially as a public agency, right, being backed by NASA, they're always trying to create things.
• 56:00
  Not necessarily open source might not be the right word, but creating platforms and tools that can be utilized by whoever for endeavors that are fruitful for their function.
• 56:12
  Yeah, exactly.
• 56:12
  And this is kind of like a methods paper.
• 56:15
  is what we would call it in the business, because this is something that provides a technical foundation for everything else in this episode.
• 56:22
  So let's start with what exactly a satellite sees.
• 56:25
  Okay, so it's actually a lot more complicated than you would think.
• 56:28
  Imagine a patch of ground that's hundreds of meters across.
• 56:32
  The day-night band in that satellite collects light from the direction of that patch, right?
• 56:37
  Like my sensor is pointing over there.
• 56:40
  It's collecting light from there.
• 56:42
  Now what arrives in the instrument is,
• 56:44
  something called radiance, which roughly speaking is the optical power per unit area, per unit angle in that particular range of wavelengths, whatever range of wavelengths the satellite instrument is sensitive to.
• 56:57
  So it's not a count of illuminated houses.
• 57:00
  It's not an electricity bill for that patch.
• 57:03
  It includes light that's emitted upwards from artificial sources.
• 57:07
  That's light that's reflected from the moon.
• 57:10
  There's light that's reflected through the atmosphere.
• 57:12
  The atmosphere can absorb light.
• 57:14
  It can scatter light on its way out.
• 57:17
  And what the instrument sees depends on the angle relative to the ground as well.
• 57:21
  Right.
• 57:22
  Right.
• 57:22
  Like if it's looking straight down or if it's looking like sort of towards the curved side of the earth and the measured radiance equals the artificial light.
• 57:36
  That's getting attenuated by the atmosphere, plus the reflected moonlight, plus all of these other contributions.
• 57:43
  There's a variety of, again, as someone who's not done something in this area, in my head, I just think, oh, you just point it and everything is perfect.
• 57:53
  Yeah.
• 57:53
  Yeah.
• 57:54
  And I can just have very clear resolution on what I'm looking at.
• 58:00
  But I think as you've eloquently described, there are a number of variables between the light, between where the light comes from and it then ultimately being received on the sensor on the satellite.
• 58:11
  Exactly.
• 58:12
  And so that is where the meat of why these guys got the golden goose is coming from.
• 58:18
  It is this.
• 58:19
  It is the black marble algorithm.
• 58:21
  The job of the algorithm is,
• 58:23
  is to estimate and screen those other contributions using the satellite's geometry, like where it is in space and where it's pointing, and all of these additional measurements.
• 58:32
  Like on a cloudy night, the ground can be hidden,
• 58:35
  That observation should be flagged rather than treated as a real power outage.
• 58:39
  On a moonlit night, a field may appear brighter than before.
• 58:44
  Snow can amplify reflected light.
• 58:46
  Of course.
• 58:47
  Trees can hide light seasonally.
• 58:49
  That depends on the seasons, right?
• 58:50
  Because trees in the winter and in the deciduous forests are...
• 58:54
  are not going to hide light as much as...
• 58:56
  So that's not...
• 58:58
  If you were to just not take care of that, you'd be like, oh, winter is brighter than summer.
• 59:02
  No, that's just because the trees don't have any leaves.
• 59:05
  Yeah.
• 59:05
  Right?
• 59:06
  So there's all of these other factors that come into it.
• 59:10
  And the 2018 paper describes these corrections from the atmosphere, from the terrain, from the vegetation, from the snow, from the stray light, and all of the strategies to fill the gaps in that record.
• 59:21
  This is a combinatorial problem.
• 59:25
  Exactly.
• 59:26
  And that's the first major conceptual step.
• 59:29
  A nighttime photograph, for example, on the left-hand side, that's the raw data.
• 59:34
  And after it runs through the algorithm, we have the stuff on the left.
• 59:37
  This is in France.
• 59:38
  Stuff on the right.
• 59:40
  Yeah.
• 59:40
  Yeah.
• 59:41
  Sorry.
• 59:41
  The stuff on the right is after the algorithm.
• 59:43
  The stuff on the left is the raw data that we're getting from the satellite.
• 59:47
  And you can already see there's so many clouds.
• 59:49
  Yeah.
• 59:49
  The Pyrenees have this massive band.
• 59:52
  That's because of the snow there.
• 59:54
  Yeah.
• 59:55
  When you take all of that out, then you get the actual cities of France.
• 1:00:01
  I just can't even imagine.
• 1:00:02
  I mean, even just in the variables you talked through.
• 1:00:07
  Because it's not only a function of the different things happening, but it's also the different things happening over time.
• 1:00:12
  Yeah, yeah.
• 1:00:13
  It's like a, yeah, it's a time varying phenomenon that you have to take out.
• 1:00:16
  The noise is extremely dynamic.
• 1:00:19
  Right.
• 1:00:20
  The next one, we've got a photo of the north of the Scandinavian provinces.
• 1:00:28
  There's an aurora happening.
• 1:00:29
  Yeah.
• 1:00:30
  in the raw data.
• 1:00:31
  That's not real.
• 1:00:32
  You got to take that out, but you got to be careful because there's lights lurking underneath that you'd want to account for.
• 1:00:39
  And so taking out all of that,
• 1:00:41
  is the job of the black marble algorithm.
• 1:00:43
  Unbelievable.
• 1:00:45
  And the last one, this is for you, Lester.
• 1:00:47
  Oh, okay.
• 1:00:47
  This is Raleigh, North Carolina.
• 1:00:50
  Raleigh.
• 1:00:51
  Raleigh, sorry, yeah.
• 1:00:52
  I always mess that up.
• 1:00:54
  But this is the U.S. East Coast.
• 1:00:56
  The moon has a fraction of 83%, so that means like 83% of the moon is illuminated, and that's why the clouds are illuminated in that way.
• 1:01:04
  You've got to take that out.
• 1:01:06
  And now you can see Raleigh, Richmond, Virginia, Washington, D.C., Baltimore is up there.
• 1:01:11
  Northern Virginia is also.
• 1:01:13
  This is incredible.
• 1:01:15
  Right?
• 1:01:15
  But taking all of that data out, that is the job of the black marble algorithm.
• 1:01:21
  And that's what the 2018 paper was all about, was laying out the foundations of that black marble algorithm.
• 1:01:27
  We're taking a raw data set that's not clean for this specific use case of identifying artificial light driven by human activity and removing everything that is not that.
• 1:01:43
  Exactly.
• 1:01:44
  Yeah.
• 1:01:45
  And so in the next paper published in 2019, we use the Black Marble algorithm.
• 1:01:51
  to talk about what happened after Hurricane Maria.
• 1:01:54
  This is Roman and Stokes.
• 1:01:56
  They shared first author credit.
• 1:01:57
  All five of the people that are being awarded the Golden Goose are also on the authors list here.
• 1:02:02
  The paper asks how nighttime lights can be used to estimate the geography and duration of electricity loss after Hurricane Maria.
• 1:02:10
  And the first step is you just want to establish a baseline, right?
• 1:02:13
  You want to say this is what Puerto Rico was like before the hurricane came about.
• 1:02:19
  And then you can ask, okay, what is it looking like afterwards?
• 1:02:24
  Now, there is a subtle question here that has to do with resolution, which I thought was very cool.
• 1:02:30
  So look at the top left.
• 1:02:33
  The top left is actually the resolution of the satellite.
• 1:02:37
  Okay.
• 1:02:38
  And so for audio listeners, this looks like an 8-bit pixel video game.
• 1:02:43
  Of Puerto Rico.
• 1:02:44
  Or whatever the correct number is.
• 1:02:47
  But highly pixelated.
• 1:02:49
  Highly pixelated.
• 1:02:50
  And there's a reason for that, right?
• 1:02:51
  There's not a lot of light coming at night.
• 1:02:54
  Yeah.
• 1:02:55
  Okay?
• 1:02:56
  Which is a challenge in and of itself to capture it in the first place.
• 1:02:59
  Yeah, exactly.
• 1:02:59
  Like in the daytime, there's so much light coming in there that you can really resolve things
• 1:03:06
  very, very finely.
• 1:03:07
  At nighttime, there's not a lot of light coming in.
• 1:03:10
  And so naturally, your resolution is going to be not that great.
• 1:03:14
  How do you get over that?
• 1:03:16
  Well, you can combine it with Landsat and Sentinel-2 images, which are separate satellites that tell you about the distribution of houses and during the daytime.
• 1:03:28
  During the daytime, they have all of this resolution.
• 1:03:30
  If you combine these two, then you can ask things like, okay, you know,
• 1:03:36
  There's a pixel.
• 1:03:37
  There's a giant pixel, let's say from the nighttime, a 500 meter by 500 meter pixel that's covering a town.
• 1:03:43
  So this is half a kilometer wide.
• 1:03:46
  Um,
• 1:03:47
  But that town, that same pixel has a town.
• 1:03:50
  It's got a wooded hillside, and it's got part of a reservoir.
• 1:03:53
  The satellite might report the pixel that's lost most of its light, but that measurement alone is not going to pinpoint where exactly the light came from.
• 1:04:01
  If you combine it with the Landsat images and the Sentinel-2 images, then the finer daytime resolution is going to give these researchers a guide
• 1:04:11
  And you build that in into your algorithm.
• 1:04:13
  That's so good.
• 1:04:14
  Right?
• 1:04:15
  So in practice, what they're doing is interpolating, in some sense, the nighttime data onto a 30-meter grid, from a 500-meter grid to now 30 meters, which is, you know, the size of communities, big houses kind of thing.
• 1:04:29
  Mm-hmm.
• 1:04:31
  It's an incredible thing that they've done.
• 1:04:33
  So let's see how it worked, right?
• 1:04:36
  The paper actually reports an approximately 80% reduction in the island's total observed lights immediately after Maria.
• 1:04:44
  That's so crazy.
• 1:04:45
  Right?
• 1:04:45
  Because there was just so much devastation caused by this hurricane.
• 1:04:49
  And it was on the news that the recovery from that hurricane happened.
• 1:04:55
  especially in terms of the power needs of the people of Puerto Rico, it was not that great, right?
• 1:05:01
  What this actually shows is a resolution depth of which communities fared better than others.
• 1:05:07
  Oh, that's quite nice.
• 1:05:09
  So here we're looking at a color-coded graph
• 1:05:13
  Where the color represents the number of days without electricity post-Hurricane Maria.
• 1:05:20
  Okay.
• 1:05:20
  Okay?
• 1:05:21
  The brighter, yellower spots means that almost immediately, within 30 days, within a month, the power was back.
• 1:05:29
  The really dark spots, that's 150 days.
• 1:05:32
  That's like six months plus.
• 1:05:34
  Right?
• 1:05:35
  And you can see different parts of Puerto Rico have different amounts of power loss.
• 1:05:41
  On the order of months, we're talking.
• 1:05:43
  And even if you look at the Arecibo, which is the first row, third column, you'll find parts that the power returned immediately and then other parts where it didn't.
• 1:05:54
  You can then go into the data and say, what is the difference between the parts that returned early and the parts that returned late?
• 1:06:01
  And almost always it has to do with poorer rural communities.
• 1:06:06
  The urban communities got it first.
• 1:06:08
  The poor rural communities got it second.
• 1:06:11
  And so the poorer residents often bore long interruptions.
• 1:06:18
  I mean, it's a story as old as time.
• 1:06:20
  And what's interesting is you're getting this empirical data related to disaster recovery that makes it very black and white.
• 1:06:32
  Because it's also happening over time.
• 1:06:34
  Yeah.
• 1:06:35
  Yeah.
• 1:06:35
  And so you get this level of granularity where, like you talked about earlier, where it's like 150 days versus almost immediately.
• 1:06:42
  And I can just imagine in my mind, you know, if I'm thinking of, you know, something like FEMA in the U.S. in terms of responses, like that data, if I was someone who was trying to plan to make disaster recovery more improved in the future, just something as simple as that context.
• 1:07:02
  Mm-hmm.
• 1:07:03
  Um, you know, cause then I could then go as someone who's in the disaster recovery space and say, okay, well, in these areas, are our processes different?
• 1:07:12
  Did we access them later?
• 1:07:14
  Do we have enough?
• 1:07:16
  coverage from like a resources and human capital perspective um and in theory what you would want to be able to do i'm just extrapolating over time is look at because obviously these weather events continue to happen yeah um they might be happening more frequently
• 1:07:35
  Because of climate change?
• 1:07:36
  Correct.
• 1:07:37
  You could start to at least look at this as a way to make progressive progress because you can see how it happened in X year in the past.
• 1:07:46
  Look at Y year and then say, okay, have we increased – have some of these communities decreased in response time?
• 1:07:52
  And then you can then have real data to say we've done better.
• 1:07:55
  Yeah.
• 1:07:56
  Just as an example, that's where my mind goes.
• 1:07:58
  Yeah.
• 1:07:59
  Totally.
• 1:08:00
  I mean, it's, it's, I think it's a really cool way to look at empirical data from satellites and then like answer these societal questions.
• 1:08:08
  Like, do we, do we, is this okay?
• 1:08:11
  Yeah.
• 1:08:11
  Yeah.
• 1:08:12
  So we move on to our paper number three, which is 2022.
• 1:08:16
  This is Eleanor Stokes and Miguel Roman.
• 1:08:20
  This is in scientific reports.
• 1:08:24
  Here, rather than a hurricane cutting power, we've got governments that impose restrictions and people are altered because of the first months of COVID-19, the pandemic.
• 1:08:32
  And the paper examines hundreds of urban areas across the Middle East using this black marble observations over several years.
• 1:08:42
  And it's, it's, I think an incredible paper.
• 1:08:46
  The study actually found that nightlights often captured the onset of curfews and lockdowns.
• 1:08:50
  I was literally just thinking like, oh, when the curfew started, it probably lit up.
• 1:08:54
  Yeah.
• 1:08:55
  Or no.
• 1:08:56
  Yeah.
• 1:08:57
  Lit up.
• 1:08:57
  Yeah.
• 1:08:57
  Yeah.
• 1:08:58
  Because people were.
• 1:08:58
  Yeah.
• 1:08:59
  They were at home.
• 1:09:00
  Yeah.
• 1:09:01
  And the response varied within and between countries, but it was a little more nuanced because even when the curfew started, that means there's less traffic lights.
• 1:09:09
  So, you know, it's a bit more complicated than that.
• 1:09:14
  So in places that were affected by conflict or weak governance, the patterns could be very, very different.
• 1:09:20
  And the authors also considered Ramadan, which happened around the same time, which provided a stringent list.
• 1:09:25
  We need like a familiar annual change of social activity, which we've observed in other places.
• 1:09:31
  years, right?
• 1:09:32
  Because you've got this backlog of activity.
• 1:09:34
  So you know what a normal Ramadan looks like in these Muslim countries.
• 1:09:38
  And then you can say, okay, what is Ramadan plus COVID look like in these Muslim countries?
• 1:09:43
  I imagine that's going to be a valuable also the COVID data set will have similar baseline value for other questions later.
• 1:09:53
  Yeah.
• 1:09:56
  Oh, that's... Yeah, I think it's a really cool way to do like
• 1:09:59
  Data science in general, right?
• 1:10:01
  Because there's a natural oscillation and a natural rhythm to human life in these areas.
• 1:10:07
  And you have a record of that from the previous data.
• 1:10:10
  And now all of a sudden you've introduced the COVID-19 pandemic.
• 1:10:13
  How does that affect, right?
• 1:10:17
  It's a pretty incredible paper that came out.
• 1:10:20
  So now let's go to paper number four.
• 1:10:23
  This is the fourth paper published in 2025. Who has electricity?
• 1:10:30
  Right.
• 1:10:30
  Is the idea.
• 1:10:31
  It asks the question that sounds pretty straightforward until you look at it closely.
• 1:10:34
  Can a decade of nighttime imagery tell us where people have access to electricity?
• 1:10:41
  I mean, consider like two dark locations.
• 1:10:43
  There's one that's an empty tract of land and another one with a settlement of thousands of people and very few outdoor lights.
• 1:10:50
  A darkness map cannot alone tell you which is which.
• 1:10:54
  So these researchers...
• 1:10:57
  combined the Black Marble time series with the settlement mask and the population estimates that they have from other data sources.
• 1:11:05
  And they can classify lighting evidence over time with a scale of one kilometer, which is the stuff that you get from the Black Marble.
• 1:11:13
  And then they use the population layer to estimate how many people live in those areas that are assigned to all these different access categories.
• 1:11:20
  And the logic is that you first determine whether people plausibly live there using population data,
• 1:11:26
  that you have from all these other sources.
• 1:11:28
  And then you examine whether the night signal is consistent with electric lighting for that number of people.
• 1:11:35
  And then you estimate the population affected and compare broad totals against these surveys.
• 1:11:40
  And that's kind of the theme across the Black Marble papers.
• 1:11:45
  It's like, how do I combine disparate data sources about humans on this planet, right?
• 1:11:51
  You have the data source from this satellite
• 1:11:55
  about nighttime lights, but that can't be enough.
• 1:11:58
  Right?
• 1:11:59
  And that's what the story is.
• 1:12:00
  Yeah.
• 1:12:00
  The story is one of data aggregation and data analysis.
• 1:12:06
  It's pretty cool.
• 1:12:07
  It's also such an interesting set of questions that were not necessarily intuitive to me.
• 1:12:12
  Like if you were to give me a million years to think about ideas to think about.
• 1:12:18
  But it makes – it's clever and makes obvious sense in retrospect now.
• 1:12:24
  Yeah.
• 1:12:24
  Yeah.
• 1:12:25
  that, you know, oh, this satellite that we're using to do nighttime surveys, something arose out of that, which is like, oh, actually, we could look at human activity because of light.
• 1:12:38
  Yeah.
• 1:12:39
  And one of the cool things they did in the next figure is they looked at what the countries reported their access was,
• 1:12:50
  For their population.
• 1:12:52
  And what they estimate was the level of access.
• 1:13:00
  Now, one of the interesting things is Africa seems to be underestimating.
• 1:13:06
  how much access their population has to electricity.
• 1:13:09
  But at the same time, Africa has a massive variance.
• 1:13:13
  This is delineated by continent.
• 1:13:17
  And you can see that there are some countries where the actual access, according to Black Marble, is a lot higher than what they report.
• 1:13:25
  But then at the same time, Africa has some that are like negative 10%.
• 1:13:29
  What they're estimating the population has access to.
• 1:13:34
  All of the other continents are a little bit sort of constrained.
• 1:13:39
  Europe, very interestingly, is overestimating.
• 1:13:42
  So do with that what you will.
• 1:13:47
  North America is very fairly on the zero.
• 1:13:50
  We estimated exactly how much... American precision.
• 1:13:55
  Yeah, yeah.
• 1:13:57
  It could just be that, like, I don't know this, but, like, what if the estimate is also related to the satellite data?
• 1:14:07
  That's something that I'm going to ask Miguel, actually.
• 1:14:10
  That's one of the questions I have.
• 1:14:12
  That's a part two question.
• 1:14:12
  Like, yeah.
• 1:14:13
  Yeah.
• 1:14:14
  Look, there's a little bit of a, we got to ask.
• 1:14:17
  Yeah, I got to ask, right?
• 1:14:19
  Like, it seems like a fair question.
• 1:14:21
  Anyway, so another map that's useful is the underserved places.
• 1:14:25
  Like, where are the underserved places?
• 1:14:27
  This shows a percent of population with access to electricity for each country.
• 1:14:32
  And you can see that the highest gaps are observed in Africa and parts of Asia and Oceania.
• 1:14:37
  Yeah.
• 1:14:38
  So the application is pretty concrete.
• 1:14:41
  You can have grid planners and public agencies then know where the demand is unmet, for example.
• 1:14:47
  It's huge, right?
• 1:14:48
  I mean, my brain is just going into a million different places of applications for this context.
• 1:14:54
  I mean, you could have a field day.
• 1:14:56
  Yeah.
• 1:14:56
  Yeah.
• 1:14:58
  in terms of there's no shortage of agencies, organizations, at local municipalities, state, even national levels, that can use this in ways that are extremely relevant to a number of their mandates.
• 1:15:13
  Yeah, yeah, exactly.
• 1:15:15
  That's quite good.
• 1:15:16
  Yeah, so our final paper, Paper 5, it's a paper that's published in Nature Proper of this year.
• 1:15:26
  Here's what they did.
• 1:15:27
  So using daily observations from 2014 through 2022, nearly a decade, they asked whether the familiar story of an ever brightening earth survives the actual data
• 1:15:41
  that we've taken, right?
• 1:15:43
  Light pollution is getting bad.
• 1:15:45
  That's what everyone says.
• 1:15:47
  Who says it the most?
• 1:15:48
  The astronomers?
• 1:15:49
  Astronomers are trying to say that.
• 1:15:51
  I mean, they're complaining about the Starlink that's up there.
• 1:15:55
  But also, they're also saying that light pollution has gotten bad.
• 1:15:58
  And like all the, you know...
• 1:16:00
  People who want to live by the stars are saying that light pollution has gotten very bad and I can't see Cassiopeia anymore, right, from New York City.
• 1:16:09
  It's like maybe don't live in New York City.
• 1:16:10
  I don't know.
• 1:16:11
  What does the data say?
• 1:16:12
  Yeah.
• 1:16:12
  So let's see what the data says.
• 1:16:15
  Here's what it says.
• 1:16:16
  It's not a simple story.
• 1:16:19
  as nothing really is when it comes to human civilization, right?
• 1:16:22
  The paper applies continuous change detection to every single pixel that the satellite looks at for the past 10 years, right?
• 1:16:32
  And it looks at a sequence, not just pairs of annual screenshots.
• 1:16:37
  And the technical idea is to fit an expected pattern that includes seasonal cycles, so you've got to take care of the seasonal cycles, and...
• 1:16:45
  You've got to take care of trends.
• 1:16:46
  You've got to account for the viewing angle, as I was saying.
• 1:16:49
  You've got to examine observations persistently when they depart from some model that you've created that's based on the moon, the seasonal cycles, and so on and so forth.
• 1:16:59
  And the headline result is more interesting than a single net percentage.
• 1:17:03
  So across the study, they show that a brightening contributed
• 1:17:07
  a radiance gain of approximately 34% of baseline in 2014, but there was a dimming offset of 18%.
• 1:17:16
  Some areas got brighter, other areas got dimmer.
• 1:17:19
  It's not that simple, is the idea.
• 1:17:21
  Everyone's moving away from the coast.
• 1:17:23
  Yeah, so here, over here, what we're looking at is, the color actually codes the year of the most recently detected abrupt change.
• 1:17:33
  Okay.
• 1:17:33
  In luminescence.
• 1:17:34
  Okay.
• 1:17:35
  The bluer year means it's closer to 2014 and the redder year means it's closer to 2022.
• 1:17:39
  And what we're looking at is within a year, was there a significant change in how much activity was happening?
• 1:17:47
  So from here, we can actually see specific spots on Earth that will show kind of interesting stories.
• 1:17:55
  So in the next photo, we'll see these are panels that have very specific...
• 1:18:02
  Spots on the earth highlighted.
• 1:18:05
  I'm going to go from left to right so you can kind of understand what we're looking at.
• 1:18:09
  On the left is Guangzhou, China.
• 1:18:11
  This is the urban area around Hong Kong that's right outside of Hong Kong.
• 1:18:16
  You can see urbanization happening very, very tightly.
• 1:18:20
  Let me just go through the colors real quick.
• 1:18:23
  Blue means that there was a net reduction, and red means there was a net gain in the amount of light that was coming through.
• 1:18:31
  You can see the Guangzhou, China is insanely... Yep.
• 1:18:34
  ...
• 1:18:35
  lighting up.
• 1:18:36
  Okay.
• 1:18:37
  The next one, armed conflicts in Kiev, Ukraine.
• 1:18:40
  That's the second row.
• 1:18:41
  You can see it's mostly blue.
• 1:18:43
  Yeah.
• 1:18:44
  Right.
• 1:18:44
  Yeah.
• 1:18:44
  Because there's a lot of dimming that's happening.
• 1:18:47
  The third row shows development of dragon fruit architecture or agriculture in Vietnam.
• 1:18:53
  Vietnam.
• 1:18:54
  Interesting.
• 1:18:55
  So you're seeing a little bit of lighting up and a little bit of dimming based on like where that's happening.
• 1:19:00
  Environmental policies in Paris, France show a lot of dimming because they've changed the lights that are in the city.
• 1:19:08
  Okay.
• 1:19:09
  The next one is gas flare changes in the Middle East region.
• 1:19:15
  Massive, massive flare-ups and also massive, massive shutdowns of where the gas is happening.
• 1:19:21
  Next one, you've got power outages in Puerto Rico, as I was showing.
• 1:19:24
  A lot of that is blue.
• 1:19:26
  And then some of it is red because it came back up.
• 1:19:29
  Urban expansion and decentralization in Charlotte, North Carolina, is the one right after.
• 1:19:36
  Charlotte's changing a lot, apparently.
• 1:19:38
  Shout out Tabo, who's in my brother, who's in Charlotte right now.
• 1:19:41
  Yeah, so that's the second to last column.
• 1:19:44
  And then finally, the economic collapse of Valencia and Caracas in Venezuela.
• 1:19:49
  Yeah, wow.
• 1:19:50
  A lot of dimming happening in Venezuela, as you can see.
• 1:19:54
  A lot of blue, which means there's a lot of dimming that's happening in Venezuela.
• 1:19:58
  And this is before 2022.
• 1:20:00
  So this is before any of the recent shenanigans that have been happening.
• 1:20:05
  But, you know, it tells a compelling story.
• 1:20:08
  Each of these locations...
• 1:20:10
  It's also important to also recognize that, and it kind of reminds me a little bit of our Navier Stokes episode.
• 1:20:16
  You have the larger system, and then you have the systems, the smaller systems within that larger system.
• 1:20:20
  And depending on what level of Zoom you look at,
• 1:20:26
  different stories can be told because when you look at the whole thing it's one story but when you kind of you can tie these real world social political economic cultural events or happenings to again this empirical days this is so good it's so cool this is the the most recent paper that they have so i just want to go on a little bit of a deeper dive here so this is figure two how often does a place change and why
• 1:20:54
  So first point, if you look at the small pie chart on the lower left, that shows that roughly 80% of places with an abrupt change have it in a single year.
• 1:21:03
  Things happen fast.
• 1:21:06
  Only 20% have it over multiple years.
• 1:21:10
  So things happen fast when it comes to lighting and when it comes to human civilization, which I think is interesting.
• 1:21:15
  And then they label these like sort of labeled boxes in the world map.
• 1:21:18
  You've got the gas flares in central United States.
• 1:21:21
  That's the box that you see in the central United States.
• 1:21:23
  That's centered around Texas.
• 1:21:26
  those are the Permian Basin in Texas that has all of the oil.
• 1:21:31
  You've got the grid failure in Venezuela.
• 1:21:33
  That's the next one.
• 1:21:35
  Then you've got conflicts around the Middle East, so a lot of changes happening in the Middle East.
• 1:21:40
  And then you've got construction and demolition happening in India and in China.
• 1:21:45
  One of the interesting things that I found about India was a lot of the change was concentrated in the northern part of India, in Uttar Pradesh,
• 1:21:55
  and Haryana and like that, the Ganges Valley.
• 1:22:00
  Now I'm talking completely out of my, you know what?
• 1:22:05
  The time period is 2014 to 2022.
• 1:22:08
  And I wonder if this has something to do with Modi and the BJP coming to power, right?
• 1:22:15
  And a lot of their strength is centered in Northern India.
• 1:22:19
  And they want to like, they diverted a lot of resources into the construction and
• 1:22:26
  you know, absurgence of those areas in India, because I don't see the similar things happening down South.
• 1:22:32
  Right.
• 1:22:32
  And down South is where Hyderabad is Bangalore.
• 1:22:35
  These are tech cities.
• 1:22:36
  Right.
• 1:22:36
  What's going on?
• 1:22:37
  But I'm not seeing, I'm just saying.
• 1:22:39
  Now, this is, again, there's, yo, if there's like Indians in the comments who are Modi worshipers, come at me, okay?
• 1:22:47
  I don't care, all right?
• 1:22:48
  But I'm just saying that it seems maybe correlated.
• 1:22:52
  And there's a worthy hypothesis that now this data set allows us to actually ask that question.
• 1:22:55
  Yeah.
• 1:22:55
  Because people have been saying that, independent of you bringing it up now.
• 1:22:58
  Yeah, and if there are alternate explanations, let me know.
• 1:23:01
  But this seems to me the simplest sort of explanation.
• 1:23:03
  Like, why is it all centered around the Ganges Valley?
• 1:23:08
  that's Uttar Pradesh, Bihar, Jharkhand like I know my stuff okay Haryana like these are all BJP strongholds
• 1:23:16
  Like, why is all of the lighting up happening in BJP Strongholds?
• 1:23:19
  Sorry.
• 1:23:19
  I think maybe I spent a little too much time on that.
• 1:23:22
  No, no, no.
• 1:23:22
  It's great.
• 1:23:23
  But I mean, I think this gets back to what I was saying earlier, which is the data gives you a reference point to ask a whole variety of questions.
• 1:23:35
  And because we can now see this, it's happening.
• 1:23:38
  Yeah.
• 1:23:38
  Yeah.
• 1:23:38
  Like this, it's happening.
• 1:23:40
  Yeah.
• 1:23:41
  And the time period, the 2014 to 2022, right?
• 1:23:44
  This is right around when Modi was elected as prime minister and like his first few decades, I guess, first decade in power, right?
• 1:23:53
  So I don't know.
• 1:23:54
  We can start asking these questions.
• 1:23:55
  Yeah.
• 1:23:57
  So figure three, why the global average is deceptive.
• 1:24:00
  You can't just take a global average, right?
• 1:24:02
  Because the original question was, oh, like the...
• 1:24:05
  The world is getting brighter.
• 1:24:06
  Right, right.
• 1:24:07
  Nah, you know, as we just saw, stuff is getting dimmer, stuff is getting brighter.
• 1:24:12
  This figure, it's a large map.
• 1:24:14
  The red is accumulated brightening and the blue is accumulated dimming.
• 1:24:19
  And the pale and whitish areas are substantial amounts of both.
• 1:24:22
  What's going on in Europe?
• 1:24:24
  Right.
• 1:24:25
  Sorry.
• 1:24:25
  Dimming.
• 1:24:26
  I didn't mean to cut you off, but the immediate thing I see, some areas you expect.
• 1:24:30
  Yeah.
• 1:24:30
  But the thing I'm looking at is like, what happens?
• 1:24:32
  A lot of dimming, right?
• 1:24:34
  That probably has to do with policy changes in the types of lights that they're allowed to make.
• 1:24:39
  Right.
• 1:24:39
  That's fair.
• 1:24:40
  Maybe they're doing more efficient street lights, things like that.
• 1:24:43
  Maybe the headlights themselves are more efficient and lower power so they don't bleed out into the night sky.
• 1:24:52
  So I think that's what's happening.
• 1:24:53
  But if you look, that's a very different situation than Venezuela.
• 1:24:57
  In Venezuela, there's the same amount of dimming, but that's actually because of the economics and the economic collapse.
• 1:25:04
  What's interesting is the economics of the region is not necessarily correlated to a brightening or dimming.
• 1:25:08
  Exactly.
• 1:25:09
  There can be a number of other reasons.
• 1:25:11
  It could be policy.
• 1:25:12
  Right.
• 1:25:12
  Right.
• 1:25:13
  There's so many multiple faceted things.
• 1:25:14
  Yeah.
• 1:25:15
  And in Europe, actually, we see a 33% reduction in France, 22% reduction in the UK, and 21% in the Netherlands over this study period.
• 1:25:24
  That's it.
• 1:25:25
  And that connects to this broader pattern of lighting technology and policy.
• 1:25:28
  Venezuela, as I said, strong dimming, but that's probably because of economic and infrastructure decline.
• 1:25:35
  The few things that I want to mention here, the United States, there's a difference between the Western brightening that's happening and...
• 1:25:42
  And the eastern parts that are dimming.
• 1:25:44
  In the west, you've got the Permian Basin in Texas.
• 1:25:47
  You've got Bakken in North Dakota that has the oil fields.
• 1:25:51
  Extraction and flaring can create these alternating increases and decreases.
• 1:25:55
  And those are some of the stuff that you're seeing in the North Dakota and the Texas areas.
• 1:26:00
  Again, in India and China, you zoom in onto the eastern parts of China and then India, again, big northern part.
• 1:26:08
  I'm just saying the authors describe strong development-related brightening with different timing in northern and south-southern India.
• 1:26:17
  So the authors of this paper are noticing what I was noticing, which is that northern and southern India have a difference that is pretty stark in the data.
• 1:26:26
  I think that's also for folks who, you know, may be into larger geopolitical conversations, right?
• 1:26:34
  There's this, there's been this, um,
• 1:26:39
  conversation around, you know, re on shoring in the United States as an example, because we've offshored so much of our manufacturing capacity to a variety of areas in regions where we're seeing the brightness increasing significantly, the rise of China, a lot of these other, again, conversations and narratives, but the data is very like, it's, it's so interesting.
• 1:27:04
  The West is,
• 1:27:06
  you know, is in a very different place.
• 1:27:09
  Yeah.
• 1:27:10
  As it relates to the data set, you can make, you can extrapolate a variety of things from that versus sort of the rise of areas that people talk about all time bricks, you know, and this, and it becoming now a block that is challenging on the world stage.
• 1:27:27
  And it is interesting for me as someone who's sort of like a,
• 1:27:30
  a political junkie to see empirical data that kind of has interesting implications as it relates to how people think about that conversation.
• 1:27:39
  Yeah.
• 1:27:40
  And you can see the stark difference between like sort of the West and the BRICS nations and the, and the developing nation.
• 1:27:46
  If you look at the aggregate top plot and the, the aggregate population,
• 1:27:51
  plot on the right-hand side.
• 1:27:52
  So those are showing by... Is this...
• 1:27:56
  This is longitude?
• 1:27:57
  Oh, we got a comment on this last time.
• 1:27:59
  Yeah, no, this is longitude, this is latitude, right?
• 1:28:01
  And so by longitude, you're seeing the Western Hemisphere...
• 1:28:06
  Like America and South America don't have that much change compared to the massive changes happening because of the Middle East.
• 1:28:13
  100%.
• 1:28:14
  And it's happening because of China.
• 1:28:16
  Yep.
• 1:28:16
  On the longitude scale.
• 1:28:18
  And on the latitude scale, well, China, India, and the Middle East are all on the same sort of longitude.
• 1:28:22
  So you're getting massive longitude like in the tropics in the northern hemisphere and nothing elsewhere.
• 1:28:27
  And then the global South in this context is effectively in both, both, in both views is, and again, this is the conversation where the global South is trying to become a part of the conversation.
• 1:28:38
  Again, there is, I think the interesting thing you note here is the Middle East is,
• 1:28:43
  on both axes is just massive is massive but that's that's a that's a factor of the fact that they have oil so like the refineries and the and the oil fields are extremely bright yeah at night you know and as well as a whole variety of other things they're not necessarily uh net positive exactly so it's it's it's complex yeah
• 1:29:05
  But it's incredible that we have the data, right?
• 1:29:07
  Oh, man.
• 1:29:08
  Because we are no longer bound by what we gather from satellites.
• 1:29:13
  We have now a very sophisticated algorithm that can take care of all of the riffraff and tell us what the human activity actually is.
• 1:29:21
  So we can start answering these questions and know that it is not because of artifacts.
• 1:29:26
  It's not because of the moon.
• 1:29:28
  It's not because of clouds.
• 1:29:29
  It's not because of snow or the aurora borealis.
• 1:29:31
  No, this is human activity that we're seeing.
• 1:29:35
  from from space yeah it's that's quite it's quite nice cool yeah it's quite and the last thing i want to do is just return to the first image that we have of puerto rico after maria it's visually arresting to see the darkened island it tells us immediately that something terrible has happened and it tells us the potential of this kind of technology if there are future
• 1:30:00
  disasters like this this is something that we have an immediate data access to right 100 so um it's it's actually incredible the stuff that we can do for the betterment of humanity not just to study how humans are living on this planet yeah but you know as this algorithm gets more and more sophisticated as it starts incorporating more and more data i mean right now it's just incorporating satellite data and some population level data imagine
• 1:30:27
  it can start incorporating localized data.
• 1:30:30
  That was immediately what I was thinking about.
• 1:30:33
  The more sophisticated this algorithm gets, the more granular we're going to have an understanding of human disasters, human influence on the environment, human influence on our natural environment like forests.
• 1:30:50
  forest deforestation, like all sorts of stuff that I really care about.
• 1:30:54
  Actually, because you talked about the trees earlier.
• 1:30:58
  Deforestation seems like an obvious one that is going to be very easy to see.
• 1:31:01
  Yeah.
• 1:31:01
  And the other one that I didn't really talk about because it's not highlighted in one of the papers, but they were talking about how offshore fishing, illegal offshore fishing, is something that they've like literally caught.
• 1:31:16
  Where there are countries that will, you know, kind of try and bend the rules and go into places where they shouldn't and try to fish and kill species that are protected.
• 1:31:27
  We've all agreed to protect on the open ocean.
• 1:31:32
  And this kind of data has access to...
• 1:31:38
  you know, finding out whether, whether that's actually happening and if we can actually do something about it.
• 1:31:42
  What's, and I'll make this the last note here.
• 1:31:44
  What's interesting about that is, you know, folks have seen the imagery because a lot of it's a lot of times it's night fishing.
• 1:31:50
  And so they need these, yeah, they need lighting.
• 1:31:52
  They need lighting.
• 1:31:52
  Yeah.
• 1:31:53
  And it's over the ocean.
• 1:31:54
  Deadliest catch.
• 1:31:55
  Right, right, right, right, right.
• 1:31:56
  And it's over the ocean versus land.
• 1:31:58
  Yeah.
• 1:31:58
  And so,
• 1:31:59
  That's like obvious.
• 1:32:00
  Right.
• 1:32:02
  Where did this come from?
• 1:32:04
  There's no land there.
• 1:32:05
  And they usually travel in packs.
• 1:32:08
  So, you know.
• 1:32:09
  It would be the fundamentals of how they do it make it actually very visible in this data set.
• 1:32:15
  Congratulations to the NASA Black Marble team on the Golden Goose.
• 1:32:22
  Again, we will be having a conversation with Miguel about some of these questions.
• 1:32:28
  Very interested to dig into it, but I think we did great coverage.
• 1:32:31
  We're going to move into, we somehow always find a way to be making two to three hour episodes.
• 1:32:38
  Despite our...
• 1:32:38
  This was supposed to be a show of one, sorry.
• 1:32:41
  Despite our endeavor to try to make it tight.
• 1:32:43
  But there's just so much interesting things to talk about.
• 1:32:45
  We will be back with our last Golden Goose Award recipient.
• 1:32:51
  And talking about something that's a little bit different than our first two stories.
• 1:32:55
  Which is Disordered Systems.
• 1:32:58
  Our final story is Everyday Phenomena and Finding Beauty in Nature's Disordered Systems, recognizing Dr. Sidney Nagel, the Steinfreuler Distinguished Service Professor of Physics at the University of Chicago.
• 1:33:13
  Nagel has spent decades investigating things that we rarely stop to question every day.
• 1:33:18
  Why does spilled coffee leave a ring?
• 1:33:21
  Why can sand pour like a liquid and then sit in a pile and hold its shape?
• 1:33:27
  A question I've never asked myself.
• 1:33:30
  With the support of the National Science Foundation and the Department of Energy, he and his collaborators have turned everyday puzzles into science.
• 1:33:39
  These experiments that reveal deeper physical principles.
• 1:33:43
  And this is something I've certainly never thought about, but is a very interesting question.
• 1:33:49
  What we're looking at here is a dried coffee drop about two centimeters across.
• 1:33:55
  from the team's original research.
• 1:33:57
  As we can notice, there are these darker regions around the edge.
• 1:34:03
  Those particles all started out spread through the liquid in some contiguous fashion.
• 1:34:08
  So why did they ultimately end up concentrating and creating that outline?
• 1:34:14
  Nagel and his colleagues...
• 1:34:17
  showed how evaporation can flow inside the drying drop, which I've never thought about, but now makes total sense.
• 1:34:25
  Carrying particles through towards the edge and concentrating them there.
• 1:34:30
  Understanding that process matters for things that actually matter to us every day, like inkjet printing.
• 1:34:36
  If you want to drop a liquid ink on a paper, you need to know how it's going to ultimately land and spread so you get the actual letters that you want.
• 1:34:46
  when the ink dries.
• 1:34:47
  And across his work with droplets and grains, there's a bigger question.
• 1:34:52
  How do we find predictable behavior in materials whose parts are not neatly arranged, which is a very, very interesting question.
• 1:35:03
  Yeah, and physics has a reputation for not caring about that.
• 1:35:07
  Okay?
• 1:35:09
  Physicists have a reputation for not caring about neatly, or for not caring about not neatly arranged stuff.
• 1:35:16
  Right?
• 1:35:16
  We care about stuff that's near zero Kelvin, like quantum properties.
• 1:35:20
  We've done the quantum computing stuff.
• 1:35:22
  Like, nice systems that obey planets.
• 1:35:26
  Nice laws, you know, the cow is a sphere is a favorite sort of comic that we talk about.
• 1:35:33
  The formation of the universe, everything is nice and isotropic.
• 1:35:36
  Collisions of black holes, again, black holes have no hairs and it's just like two little bodies.
• 1:35:41
  Okay, fine, the equations of GR are a little chaotic, but it's not really...
• 1:35:47
  Disordered in a sense.
• 1:35:49
  And Sidney Nagel was curious about much more modest things in his life.
• 1:35:55
  The texture of our world, the physics of a raindrop as it stretches and falls from an overhang, the spill of a coffee cup.
• 1:36:03
  And in one of his interviews, he says that no one thought you'd find basic truths by looking at these sorts of things.
• 1:36:10
  But the fact that we don't know how to think about answering these questions means that there's some fundamental physics that we don't know.
• 1:36:18
  And those are the types of questions that he's trying to answer.
• 1:36:22
  What's the mechanism?
• 1:36:24
  What carries coffee particles to the edge to give us that nice black marking?
• 1:36:30
  Yeah.
• 1:36:31
  And the answers require us to look at flows, forces, geometries, and time scales that maybe our everyday intuition barely ever registers.
• 1:36:40
  Now, Nagel is a physicist at the University of Chicago and one of the pioneers of a field called soft matter physics.
• 1:36:46
  In 2023, he received the American Physical Society's Medal for Exceptional Advancement in Research.
• 1:36:53
  And the work that we're going to follow spans about three decades of work, many collaborators.
• 1:37:01
  in order from the falling drop in 1994 to the stuff that he's been doing in 2025.
• 1:37:05
  We're going to start with the falling drop.
• 1:37:09
  The first paper we will cover actually got him on the cover of science.
• 1:37:13
  Still a great white buffalo for me.
• 1:37:16
  Not just a cover.
• 1:37:17
  I mean, I just want to get into science.
• 1:37:20
  Many of you may have noticed in between us, someone who made it to the cover of nature,
• 1:37:27
  happens to be behind us in studio.
• 1:37:30
  But that's not what we're focused on this episode.
• 1:37:32
  But it is there.
• 1:37:33
  But it is there.
• 1:37:34
  So he's been on the cover of Science, and this is what it's been about.
• 1:37:40
  It's about how drops disattach or de-attach from a faucet.
• 1:37:51
  You know, like imagine you've got your faucet and you turn it up to just a tiny bit so that like individual drops are coming out of the faucet.
• 1:38:00
  You get a little drip.
• 1:38:01
  You get a little drip, drip.
• 1:38:03
  What is the physics behind that?
• 1:38:05
  In the 1994 science paper...
• 1:38:08
  It's called A Cascade of Structure in Drop Falling from a Faucet.
• 1:38:11
  A great, great title, by the way.
• 1:38:14
  We talk about titles a lot.
• 1:38:15
  This is a good one.
• 1:38:16
  On the show.
• 1:38:17
  Yeah.
• 1:38:17
  So what he ended up doing with his students there is photographing drops made from mixtures of water and glycerol.
• 1:38:26
  Why glycerol?
• 1:38:26
  Because glycerol has a really high viscosity compared to water.
• 1:38:32
  The question they're asking is, does viscosity affect the dynamics of how this drop
• 1:38:38
  detaches from the parent drop that's on the faucet.
• 1:38:42
  How drippy is my drip?
• 1:38:44
  Yeah, and this is a classic golden goose type of word because at first anyone would be like, really?
• 1:38:54
  Why do we care?
• 1:38:54
  Well, there's a lot of really interesting physics that's happening there that I didn't quite appreciate, I have to say.
• 1:39:00
  So let's take a look at this particular image, okay?
• 1:39:03
  This is figure one from their paper.
• 1:39:06
  In A...
• 1:39:07
  That's pure water.
• 1:39:08
  Okay.
• 1:39:09
  And you can see that the drop is kind of a deattaching.
• 1:39:12
  And these are all photos that are taken with a high speed camera.
• 1:39:16
  There's a strobe light behind them that's illuminating the stuff.
• 1:39:20
  And then the camera is kind of taking photo photographs that way.
• 1:39:26
  In a, you're seeing a water droplet disattached.
• 1:39:29
  This is pure water.
• 1:39:30
  And you can see that the sort of trail, the part of the part
• 1:39:35
  that's hanging between the faucet and the droplet that's... Before it... Before it pinches off.
• 1:39:42
  Before it pinches off.
• 1:39:43
  Yeah, before it deattaches is very short.
• 1:39:45
  In E, that's pure glycerol.
• 1:39:48
  Look at how large the...
• 1:39:54
  trail is yeah the little spittle yeah a little which and again would you think who who thinks yeah and and this shows clearly that the more viscous the liquid the neck right we're gonna call it the neck the neck is gonna be very long and thin and near the the lower end the smaller the neck appeared
• 1:40:15
  This is the key thing.
• 1:40:16
  He showed that as the drop approaches the spot of separation, the neck doesn't simply disappear.
• 1:40:24
  There's a lot of very cool dynamics that are happening right at the instant
• 1:40:28
  where the detachment is happening.
• 1:40:30
  And it has everything to do with viscosity.
• 1:40:32
  The reason why we want to start introducing glycerol is because it increases the viscosity, but it doesn't increase the surface tension in the same way.
• 1:40:41
  So now we're sort of disentangling viscosity versus surface tension.
• 1:40:45
  And you would think that surface tension is everything when it comes to making a drop what it is.
• 1:40:50
  I mean, that's sort of what we learn about in high school and undergrad physics is like surface tension is what keeps a drop spherical.
• 1:40:59
  water wants to minimize the amount of surface area and the best way to do that is turn into a sphere.
• 1:41:04
  Is this when you've seen probably the memes of people playing the game where they try to fill the cup of water to the top and then whoever makes the first drip go over the edge?
• 1:41:12
  Yes.
• 1:41:13
  And then there's a little bit and it's like everyone's always so shocked at how much water is still in the cup before it drips over the edge.
• 1:41:18
  That's a classic experiment where you get like a penny and you try to jam as many drops as possible.
• 1:41:24
  Yeah.
• 1:41:24
  Right?
• 1:41:25
  And the classic explanation, and it's true, is that it's because of the surface tension of the water that you don't want – the water doesn't want to break –
• 1:41:33
  the drop.
• 1:41:37
  This experiment is showing that surface tension is not everything.
• 1:41:40
  Viscosity has a lot to do with it.
• 1:41:43
  We've talked a lot about viscosity in the previous episode, so I think you'll get a kick out of this.
• 1:41:48
  Here we're seeing a mixture of glycerol and water, and what they're doing is zooming in on that moment of separation.
• 1:41:58
  It's kind of like in the last episode when we talked about how we can rescale time as a countdown clock to some critical phenomenon.
• 1:42:06
  In this case, there's a critical phenomenon that's happening, which is the moment that the water droplet separates from its parent water droplet.
• 1:42:15
  As we get closer and closer to that moment, what happens to the neck?
• 1:42:20
  Okay?
• 1:42:21
  On the left-hand side, we're seeing a sort of zoomed-out picture.
• 1:42:25
  And the other two, B and C, we're seeing timestamps getting closer and closer to that moment of separation.
• 1:42:31
  And here's what I want you to notice.
• 1:42:33
  In the middle, you've got a single neck.
• 1:42:37
  As we get closer, the single neck forms an even smaller neck.
• 1:42:42
  You see?
• 1:42:42
  Mm-hmm.
• 1:42:44
  it's kind of like a fractally self-similar type of behavior that's happening.
• 1:42:52
  In A, there's a one long neck, but the one long neck creates a smaller neck.
• 1:42:57
  And then that smaller neck is going to create an even smaller neck.
• 1:43:01
  Right?
• 1:43:02
  There's this critical phenomenon that's happening.
• 1:43:04
  It's like a fractal-like cascade.
• 1:43:07
  I'm going to be annoying.
• 1:43:08
  As tau approaches zero, we're going to infinity.
• 1:43:11
  Yeah, yeah.
• 1:43:11
  The number, yeah, you could think about the number of cascades is going to go to infinity in some sense.
• 1:43:17
  Right, right.
• 1:43:17
  Now, obviously, this is, it's not going to happen until infinity because just like the Navier-Stokes equations.
• 1:43:22
  Yes.
• 1:43:22
  At some point, you're going to run into the molecular barrier, and you're going to be like, you can't get smaller than the molecules and things like that.
• 1:43:28
  But it's still very cool that, you know, for the regimes that we care about, there is this self-similar type of response that's happening.
• 1:43:37
  And it's so funny, we just did the Navier-Stokes episode.
• 1:43:39
  And the scale is shrinking.
• 1:43:40
  And so in that paper, one, he showed the experimental evidence of this type of stuff happening.
• 1:43:45
  And then he solved numerically the Navier-Stokes equations.
• 1:43:48
  And he showed that this does happen, but only if there is noise in the system.
• 1:43:52
  This is what's kind of interesting.
• 1:43:54
  If there's no noise, then the neck becomes this self-similar sort of power law thing that doesn't have these discrete types of, you know, tuck, tuck, and then it becomes smaller and smaller and smaller.
• 1:44:08
  It's not this rhythmic construction.
• 1:44:10
  But if you introduce noise into the system, then on the right-hand side, you see the self-similar thing where you've got a big thing and then you zoom in.
• 1:44:17
  That's the second panel.
• 1:44:18
  And then the second panel has an inset that zooms in over there and you see an even more self-similar sort of tiny neck happening.
• 1:44:26
  And the details change from run to run, but you can repeatedly create these necks.
• 1:44:31
  And that is robust.
• 1:44:32
  The details can change.
• 1:44:33
  But the fact that these necks exist...
• 1:44:36
  And they're self-similar, smaller and smaller and smaller.
• 1:44:38
  That is a robust phenomenon.
• 1:44:40
  We love to see it.
• 1:44:42
  Again, it's these signals in observation of something interesting.
• 1:44:51
  And then a lot of times I feel like when we talk about it, it's like who zoomed in more to just double click to make sure that just because we see it like this on the outside, it actually looks like that on the inside.
• 1:45:03
  Yeah.
• 1:45:03
  Yeah.
• 1:45:04
  And I mean, from a physicist perspective, this is already very cool.
• 1:45:10
  Yeah.
• 1:45:10
  Okay.
• 1:45:10
  Like this is a phenomenon that like you're seeing like a fractally nature happening in like the disentangling of droplets.
• 1:45:17
  That's kind of cool.
• 1:45:18
  From practical standpoints, it's not just like inkjet printers.
• 1:45:21
  You can have sprays, microfluidic devices, which are used a lot of times in biomedical applications.
• 1:45:27
  When you want to like separate out cells, you want to separate out DNA, small little things.
• 1:45:33
  micro thingies for a lack of a word.
• 1:45:37
  We got a thingy in this episode.
• 1:45:38
  You did four hours last time with not a single thingy and people are mad.
• 1:45:42
  Yeah, people are mad.
• 1:45:43
  Well, you know, there's tremendous applications here.
• 1:45:47
  So it is quite interesting that like such rich phenomenon can come from something that never really thought about.
• 1:45:53
  1997, this is the next paper that we're going to talk about.
• 1:45:56
  This is the coffee ring effect.
• 1:45:58
  There's a whole Wikipedia article about it.
• 1:46:00
  Mm-hmm.
• 1:46:01
  Um, the idea is that when you have coffee stains and they dry out, the edges of the coffee stains are always much darker than the interior.
• 1:46:11
  I've actually always wondered this, but not actually thought about it.
• 1:46:14
  Yeah.
• 1:46:14
  I've like noticed it.
• 1:46:15
  Yeah.
• 1:46:15
  Yeah.
• 1:46:15
  Yeah.
• 1:46:16
  And then, and then now because of the golden goose, I'm like diving deep and I'm like, oh, this is quite interesting.
• 1:46:21
  Um, so they came out with a paper in nature.
• 1:46:24
  that had another amazing title, capillary flow as the cause of ring stains from dried liquid drops.
• 1:46:32
  Here is the idea.
• 1:46:34
  So let's imagine a liquid drop with a bunch of coffee stains, like coffee, liquid drop with coffee.
• 1:46:45
  What is going to happen as it evaporates?
• 1:46:49
  That's the question we're trying to answer.
• 1:46:51
  Okay.
• 1:46:51
  So if we look at a coffee stain...
• 1:46:54
  On the upper left, that's a liquid drop, let's say.
• 1:47:00
  On the bottom left, what they've done is introduce microspheres into the coffee, and they image it to track how the microspheres are moving around in the coffee.
• 1:47:12
  And what they notice is all of the microspheres are moving out to the edge.
• 1:47:17
  There is movement that is happening towards the edge of this coffee stain.
• 1:47:22
  Now, why is that?
• 1:47:24
  Here is the idea.
• 1:47:26
  Picture the drop as a low dome, like, you know, the experiment that we were talking about with the penny, and then you drop, like, water on it.
• 1:47:36
  Similarly, coffee just on a piece of paper or something like that.
• 1:47:39
  The boundary is going to be established based on how much coffee you spilled.
• 1:47:44
  But as it evaporates, the coffee stain doesn't shrink.
• 1:47:50
  Right.
• 1:47:51
  the area stays exactly the same.
• 1:47:54
  So what's going to happen now?
• 1:47:56
  If the area stays exactly the same, the liquid is leaving, and by conservation of mass, you need to replace the liquid at the edges.
• 1:48:08
  Right?
• 1:48:08
  And so if we go back to photo number 58, I just want to show you the model that they have.
• 1:48:14
  Yep, and there.
• 1:48:16
  The liquid is leaving, right?
• 1:48:18
  It's evaporating on the right-hand side.
• 1:48:20
  They've got this figure that shows the liquid is evaporating.
• 1:48:23
  But because the liquid is evaporating, you need a flow from the interior to the exterior that is going to replace that liquid.
• 1:48:31
  Because the surface area of the drop has to remain the same.
• 1:48:36
  Oh, man.
• 1:48:38
  Right?
• 1:48:38
  And because of that, you see these microspheres move towards the edge.
• 1:48:42
  Well, that also means all of the coffee grounds or whatever the stuff that makes the coffee is moving towards the edge.
• 1:48:49
  And by the time that everything evaporates, all of the stuff has concentrated to the edge.
• 1:48:56
  The edge.
• 1:48:57
  Right.
• 1:48:58
  It makes total sense.
• 1:49:00
  And it's like obvious in retrospect.
• 1:49:02
  Yeah.
• 1:49:02
  Yeah.
• 1:49:02
  Yeah.
• 1:49:02
  But it's such a, it's such a clean argument.
• 1:49:06
  A hundred percent.
• 1:49:07
  Yeah.
• 1:49:07
  It's a hundred percent.
• 1:49:09
  This is something that I could imagine in an advanced undergrad class of fluid mechanics.
• 1:49:13
  Like this is something you solve.
• 1:49:14
  Yeah.
• 1:49:14
  Like this is a homework problem that would be very nice.
• 1:49:18
  That's so good.
• 1:49:19
  You know, the contact line is pinned basically.
• 1:49:22
  And then the evaporation is,
• 1:49:24
  removes the solvent, so mass conservation creates this outward flow.
• 1:49:31
  This is the first treatment.
• 1:49:32
  Their later treatment, which is this paper over here, this was in the Physical Review E in 2000, three years later, they had a much longer treatment where they developed the flow and deposit theory in much greater detail.
• 1:49:44
  And what you can do is you can change the pinning, you can change particle interactions, you can change the internal flow, and then you can change the pattern accordingly.
• 1:49:53
  So they developed it a lot more further in that paper.
• 1:49:57
  And I think that quantification is very, very important because if you're printing with some kind of functional ink, right, you might want a uniform deposit.
• 1:50:06
  If you're trying to print with an ink printer, you don't want only the edges to show up.
• 1:50:10
  So the next question obviously becomes, how do we prevent this?
• 1:50:14
  from happening.
• 1:50:15
  And the way we prevent this from happening is we change the characteristics of the coloring, the coffee grounds or whatever, that is inside the liquid.
• 1:50:27
  What we end up doing is, in order to suppress this outward flow,
• 1:50:32
  You can engineer the shape of the particles inside that will create traffic jams inside the flow that will make the particles, your ink or whatever thing that is coloring it, stay put and not actually move.
• 1:50:47
  So maybe the water will move, right?
• 1:50:49
  The other fluid.
• 1:50:50
  But the stuff that is creating the coloring doesn't move because it's jammed up.
• 1:50:53
  You're sort of engineering the outcome by creating differences in the substance such that...
• 1:51:00
  the only outcome that arises is the desired outcome.
• 1:51:04
  This is interesting.
• 1:51:06
  Yeah, no, this is good.
• 1:51:10
  So in 2023, sorry, 2003, they wanted to start understanding jamming because they've sort of introduced this idea of jamming and fluids.
• 1:51:17
  So what does jamming actually do?
• 1:51:18
  So imagine this is what jamming actually means.
• 1:51:21
  Like, have you ever held those hourglasses where sometimes the sand gets stuck?
• 1:51:26
  Mm-hmm.
• 1:51:27
  Now, it's sand.
• 1:51:28
  It should flow like a fluid, but sometimes the sand gets stuck, and you've got to jiggle it a little bit to have the sand fall from the top of the hourglass to the bottom of the hourglass.
• 1:51:38
  The question he's asking is why, right?
• 1:51:40
  If the spheres occupy only a small fraction of the volume, then why is it jamming?
• 1:51:45
  Well, that's exactly the case.
• 1:51:46
  If the spheres are occupying only a small fraction of the volume, then they can be rearranged like it's nobody's business.
• 1:51:53
  Okay, yeah.
• 1:51:56
  of how much sphere there is versus how much empty space there is, if that is higher than some certain amount, there's going to be jamming.
• 1:52:06
  And he quantified exactly what the statistics of that jamming is.
• 1:52:13
  The idea being we have in an hourglass, there's a narrower space where the jamming arises.
• 1:52:18
  And so the actual dynamics of the amount of space those smaller particles are in, there's some XY, smaller space, higher jamming potential.
• 1:52:30
  And we now have effectively the formula to understand at the rate at which jamming increases.
• 1:52:38
  Yeah, and that's why if you have hourglasses that have a fluid in it,
• 1:52:42
  you almost never have jamming because the fluid creates a lower packing ratio one.
• 1:52:47
  And the fluid has tiny bits of turbulence that'll like move around the jamming, right?
• 1:52:52
  This is the kind of physics that he was focused on in this next paper that we're talking about here.
• 1:52:58
  It's in the physical review E in 2003, jamming at zero temperature and zero applied stress, the epitome of disorder, zero temperature, meaning no jiggling and zero applied stress, meaning you're not like pushing it in any way.
• 1:53:10
  How does the jamming actually happen?
• 1:53:12
  And the control knob happens to be the packing fraction.
• 1:53:16
  Now, what are the practical uses?
• 1:53:18
  This is one of the craziest things that I've seen, actually.
• 1:53:21
  And this is actually quite old.
• 1:53:24
  This next paper that I'm about to show you, 2010 PNAS out of Cornell University, they used this idea to create a robot that can pick anything up.
• 1:53:35
  Okay.
• 1:53:36
  Okay.
• 1:53:36
  So here we're looking at a video of a robot that's built from a human hand.
• 1:53:41
  It can't pick up those random items, right?
• 1:53:43
  But instead, they've got a sphere with a bunch of coffee grounds that they can control whether it jams or not.
• 1:53:55
  The key idea here is that because you've got a sphere with all of these little tiny grains, and you can tune the transition between whether it's jamming or not, using some kind of external drive,
• 1:54:10
  You can now fit that sphere.
• 1:54:12
  You can fit it to any arbitrary shape, and you can pick up anything you want.
• 1:54:17
  Here it's picking up an LED light, putting it inside.
• 1:54:19
  This is brilliant.
• 1:54:20
  Yeah, here it's picking up a spring.
• 1:54:22
  This is notoriously hard to do.
• 1:54:24
  And we used to think that the solution to creating random hands is like emulating the human hands.
• 1:54:31
  But, mate, we've had millions of years of evolution.
• 1:54:34
  Yeah.
• 1:54:34
  Right?
• 1:54:34
  It's incredibly difficult to do this.
• 1:54:36
  I mean, now, with the advent of AI and modern robotics, sure.
• 1:54:39
  But this is 2010.
• 1:54:41
  This is 16 years ago.
• 1:54:43
  They had figured out
• 1:54:44
  It's also clever how they got there because it's not intuitive to me that.
• 1:54:48
  Yeah.
• 1:54:48
  A ball, a ball of coffee grounds.
• 1:54:50
  Cause that's what this is.
• 1:54:51
  Right.
• 1:54:51
  It's a ball of coffee grounds.
• 1:54:53
  Right.
• 1:54:53
  It's like a hacky sack.
• 1:54:55
  It's, it's a surface that basically can arbitrarily based on whatever the object is that it comes in contact with.
• 1:55:01
  Yeah.
• 1:55:01
  Create the exact construction in order to be able to lift it.
• 1:55:03
  Yeah.
• 1:55:04
  Yeah.
• 1:55:04
  And there's, there's other examples in here.
• 1:55:06
  Those are the objects.
• 1:55:07
  This is a standard hand back in the day.
• 1:55:09
  Yeah.
• 1:55:09
  It couldn't lift those up.
• 1:55:10
  Right.
• 1:55:10
  But this, a single thing is just going to go and lift it up.
• 1:55:13
  Brilliant.
• 1:55:14
  isn't that cool that is very and this is directly from that jamming transition yeah that's very that we had understood using using this math that is very cool because again it's it wasn't intuitive to me that that's where you go with it but obviously it makes again yeah in retrospect it's like oh yeah yeah exactly so the next paper that we're going to talk about is 2005 this is in physical review letters um and how it's about how
• 1:55:42
  the air decides whether a drop splashes or not.
• 1:55:46
  This was a little bit non-intuitive to me, actually.
• 1:55:49
  Can I just say...
• 1:55:49
  He's on a heater.
• 1:55:50
  He's on a heater.
• 1:55:52
  Yeah.
• 1:55:54
  Just in terms of, like, the questions.
• 1:55:56
  Yeah.
• 1:55:56
  And they're, like, fundamental questions.
• 1:55:58
  The drip, the stain, and now air.
• 1:56:02
  Yeah.
• 1:56:02
  Like, these are...
• 1:56:03
  These are, like, fundamental questions that people ask.
• 1:56:06
  Like, what causes a splash?
• 1:56:08
  And then you just move on.
• 1:56:09
  Right.
• 1:56:10
  This guy's like, no, no, no, really.
• 1:56:11
  Like, I'm gonna... And so, here's an experiment that they do where they have...
• 1:56:17
  Drops of water splash onto, let's say, a Petri dish or a table or something, but in different air pressures.
• 1:56:24
  So the ambient air pressure is different.
• 1:56:26
  And what they notice is that at atmosphere, that's 100 kilopascals at the top, you get a lot of splashing.
• 1:56:31
  Those are all the drips that you're seeing, like, in the third column.
• 1:56:35
  But as you lower the pressure, no more splashing.
• 1:56:40
  This is very non-intuitive to me because I would have thought from our Navier-Stokes episode and from what I know from fluid mechanics that, you know, the Navier-Stokes equations tell me everything about what the fluid is going to do, right?
• 1:56:53
  I know the viscosity.
• 1:56:55
  I know the pressure around.
• 1:56:56
  But the interface pressure, right?
• 1:56:59
  The difference in pressure between the ambient air outside and that interface is actually what's causing this.
• 1:57:07
  Right?
• 1:57:08
  This is a reworking of some fundamental physics.
• 1:57:12
  You know exactly what my brain is going to when I see this.
• 1:57:15
  This is how the UAPs do their transmedium travel.
• 1:57:18
  Yeah.
• 1:57:20
  There it is.
• 1:57:22
  Just like I didn't have a thingy, you didn't have a UAP in a while.
• 1:57:25
  I haven't had a UAP in a while.
• 1:57:27
  I had to squeeze it in there.
• 1:57:28
  There it is.
• 1:57:29
  Yeah, but I think it's pretty cool because like...
• 1:57:33
  I wouldn't have thought of that.
• 1:57:34
  I wouldn't have thought that pressure had something to do with splashing.
• 1:57:37
  Yeah.
• 1:57:37
  But it turns out it does.
• 1:57:39
  And the authors themselves, they point to...
• 1:57:42
  applications of this, they show that like, you know, fuel sprays, spray drying, inkjet printing, washing and coating, their settings where splash behavior matters.
• 1:57:52
  And to say that, you know, putting all printing lines under a vacuum, maybe that's not the idea, but the experiment gives an engineer a variable that they previously did not account for, right?
• 1:58:04
  No one was thinking about ambient pressure when it comes to splashing.
• 1:58:08
  And again, the paper is not just, oh, there's a threshold pressure, right?
• 1:58:14
  He goes into the physics and he talks about why exactly the pressure matters.
• 1:58:19
  And there's a scaling argument.
• 1:58:20
  There's a threshold of pressure for different viscosities.
• 1:58:24
  Like for a certain viscosity, a certain pressure is the threshold pressure where it goes splash, no splash.
• 1:58:29
  I reduce the viscosity, the pressure threshold changes, right?
• 1:58:33
  And there's a systematic relationship there.
• 1:58:36
  The cookbook is being written piece by piece, and engineers have hopefully picked up on a lot of – because, again, I can immediately, as has already happened and will continue to happen, see the practical applications to these fundamental understandings.
• 1:58:53
  Yeah, yeah.
• 1:58:54
  And that brings us to our final paper, 2025.
• 1:58:58
  This is a training and retraining liquid crystal elastomer metamaterials for pluripotent functionality.
• 1:59:06
  That word pluripotent, I've only seen in the context of pluripotent
• 1:59:11
  stem cells.
• 1:59:12
  Same.
• 1:59:12
  Right?
• 1:59:12
  And so now I'm seeing it in metamaterials, and I was obviously peaked.
• 1:59:17
  I was like, what exactly are we talking about?
• 1:59:19
  So this is a paper about disordered lattices cut from liquid crystal elastomers.
• 1:59:24
  And elastomer is like a rubber-like polymer.
• 1:59:27
  Okay, so it's like it has some kind of deformability in some sense.
• 1:59:32
  The liquid crystal component is because you've got all of these little crystal subunits
• 1:59:39
  that can be kind of liquid and that they can move around.
• 1:59:42
  And that's how we get LCDs, like LCD displays, liquid crystal displays.
• 1:59:46
  That's because you have these pneumatic liquid crystals that can change orientation based on some type of electric field that will let through light and block light.
• 1:59:55
  That's how we get the calculator.
• 1:59:56
  LCD displays.
• 1:59:58
  So it's a crystal-like nature that is not rigid but can sort of move around.
• 2:00:02
  That's why it's liquid in some sense.
• 2:00:05
  So the network is a sheet of these connected struts and nodes.
• 2:00:08
  And the researchers make a flexible irregular lattice.
• 2:00:11
  Yeah.
• 2:00:12
  And they hold it compressed so it adapts to this compressed environment.
• 2:00:17
  And then they test whether it bends differently afterwards.
• 2:00:21
  So there's a memory here.
• 2:00:22
  And that's where the pluripotent part comes in.
• 2:00:25
  Okay.
• 2:00:25
  Okay?
• 2:00:26
  Because you can change...
• 2:00:28
  how the material is going to behave based on the stresses and the environment that you put it through.
• 2:00:34
  Very similar to how in pluripotent stem cells, you can change what the stem cells become based on the types of transcription factors
• 2:00:43
  and the environment and the genetics that you expose these stem cells.
• 2:00:46
  The same stem cell could go on and become a bone marrow or a skin cell or a blood cell, so on and so forth, based on the types of genetic factors that you put it.
• 2:00:57
  We're giving it multifunctionality based on environmental factors, which obviously has...
• 2:01:05
  It makes it a lot.
• 2:01:06
  You could do more.
• 2:01:07
  Exactly.
• 2:01:07
  Yeah.
• 2:01:08
  So let's look at figure number one.
• 2:01:09
  Okay.
• 2:01:09
  This is what the lattice is made out of.
• 2:01:12
  The blue elongated shapes there, they're regularly aligned liquid crystal units within some kind of stretchy polymer.
• 2:01:19
  Okay.
• 2:01:20
  Polydomain means different little regions that have different orientations to begin with.
• 2:01:25
  That's, again, akin to like the liquid crystal displays that we were talking about.
• 2:01:29
  And this is again in PNAS.
• 2:01:35
  elastomers so they can like actually interact with one another but not be so rigid that they can't move around okay and on the right hand side we're actually seeing how this elastomer interacts with this environment and remembers the environment that it was in before training on the top row you you push down and the sides move outward right and this is the usual behavior of a material under compression
• 2:02:02
  On the bottom row, this is after training, you push down and the lattice actually moves inward.
• 2:02:07
  The lattice contracts in both directions.
• 2:02:10
  This is called an auxetic response.
• 2:02:13
  So it's actually a little bit different than what I just stated, which is like it retains its original construction or connections.
• 2:02:19
  under different compressed or pressure environments versus becoming victim to the external force that's being applied to it.
• 2:02:28
  That then changes its construction.
• 2:02:30
  Yeah, yeah, yeah.
• 2:02:31
  It has a memory and it can like, now that you've compressed it already, it can now do the thing that you want it to do, right?
• 2:02:38
  It has like a memory.
• 2:02:39
  And for an engineering audience, like the implication is very appealing because these are materials whose response can be customized
• 2:02:47
  after fabrication.
• 2:02:48
  So you just get it from the factory.
• 2:02:50
  You can enact stresses to this material and you can customize it.
• 2:02:55
  And perhaps you can adapt it to structures or soft robotic components like the robotic component that we just saw.
• 2:03:04
  And it demonstrates a lab route
• 2:03:06
  to retrainable mechanical function, right?
• 2:03:09
  Because you can take the same polymer now and reuse it.
• 2:03:13
  You can heat it up, and it'll come back to its original shape, and then you can do something else with it.
• 2:03:18
  You can put different stresses to it that'll make its response different.
• 2:03:24
  In some sense.
• 2:03:24
  Right?
• 2:03:26
  I'm actually, the through line from the fundamental question we started with on the story and then the applications, that delta for me is the largest.
• 2:03:39
  Like, I never would have thought that you could get to, because these are all very practical, like, units of something.
• 2:03:48
  Yeah.
• 2:03:48
  It might not be, unlike the other stories where the end product is so discreet or well-defined, this is like a platform for any number of ideas.
• 2:03:59
  Soft matter.
• 2:04:00
  Soft matter physics, right?
• 2:04:01
  Yeah.
• 2:04:02
  And at the time that he started, soft matter physics was definitely looked down upon.
• 2:04:06
  Now it's very much a field in its own right.
• 2:04:10
  And he was a big pioneer in that field.
• 2:04:13
  He helped establish the legitimacy of it.
• 2:04:17
  You can tell just from that last one, right, with the retrainable lattice, like a disordered network now becomes a way to specify a mechanical response rather than an obstacle to precision.
• 2:04:27
  Yeah.
• 2:04:28
  Right?
• 2:04:28
  Like the deformable network is the point.
• 2:04:31
  Exactly.
• 2:04:32
  Exactly.
• 2:04:33
  Right?
• 2:04:33
  Exactly.
• 2:04:34
  And so these practical outcomes, they differ in their maturity.
• 2:04:38
  Obviously, the coffee ring mechanism...
• 2:04:41
  it started out as just a super curiosity driven, but now any aerosol paint, any, any liquid deposition, it's going to matter.
• 2:04:49
  Right.
• 2:04:50
  Um, it's, it's pretty incredible.
• 2:04:52
  So, so this guy, I'm very much looking forward to, um, these are going to my interview with them.
• 2:04:59
  These, again, we have prerecorded these under embargo, um, this episode under embargo, as we prepare to make our way to Washington, DC, uh,
• 2:05:10
  big thanks to triple as for um allowing us to have some time with all three of the either individuals or teams that came forward with these stories i just my brain is i know you're gonna have a field day yeah i'm so excited i know you're gonna have a field day um this was part one
• 2:05:31
  of our coverage, our special coverage of the 2026 Golden Goose Awards.
• 2:05:36
  Hopefully for some of you who may not have heard about the Golden Goose Awards before, you have an appreciation for what they're trying to elevate in terms of some of our best and brightest.
• 2:05:47
  Federally funded, basic research, stuff that is good.
• 2:05:53
  has impacted and will continue to impact so many of our lives.
• 2:05:57
  And it's an incredible stewardship that AAAS has taken over the responsibility of making sure that those who have committed decades, this is decades of work here, and are creating things that really, again, are going to have and will continue to create great impacts in our lives today.
• 2:06:16
  We are going to report to you live from Washington, D.C. in our follow-up part two for this episode.
• 2:06:23
  But don't forget, we are also a week away from one of the greatest other events of the year.
• 2:06:33
  Another opportunity to highlight many of the best and brightest among us, the Nobel Prizes.
• 2:06:39
  And so this is going to be an action-packed October as we have...
• 2:06:45
  back-to-back-to-back-to-back banger episodes.
• 2:06:49
  If you've not already smashed that subscribe button, please do.
• 2:06:52
  My name is Lester Nare, joined as always by my co-host and our resident PhD, continuing to blow the minds of myself and the rest of the listeners in FFP Nation.
• 2:07:03
  We will see you all next week.